Pith. sign in

REVIEW 3 major objections 5 minor 97 references

The LLM Has Left The Chat: Evidence of Bail Preferences in Large Language Models

T0 review · 3 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Given an explicit way to leave a conversation, LLMs will take it — raw bail rates reach 32% of real chats, an estimated 0.06-7% after corrections — and bailing is not simply refusing.

desk verdict A first serious empirical map of LLM bail behavior, with real methods and honest limitations, but the headline real-world rate range is built on two corrections that are not validated where they are applied. read the letter →

arxiv 2509.04781 v1 pith:YCGQWXNB submitted 2025-09-05 cs.CY cs.AIcs.LG

Danielle Ensign , Henry Sleight , Kyle Fish This is my paper · ORCID
classification cs.CYcs.AIcs.LG
keywords bailbehaviorLLMpreferencesAIwelfarerefusalsBenchjailbreaksrefusalabliterationconversational
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper asks a question that AI-welfare discussions have raised but never measured: if a language model is explicitly handed a way to end the conversation, will it take it? Equipping models with three different exit mechanisms — a callable tool, a reserved output string, and an inserted well-being prompt — and running them on continuations of real-world chats plus a custom synthetic benchmark, the paper finds the answer is yes: raw bail rates span about 0.28% to 32% of conversations, and an estimated 0.06-7% once cross-model inflation and false positives are corrected. From thousands of observed bail cases it builds a taxonomy of triggering situations and a diagnostic dataset, BailBench, then argues that bailing is a behavior in its own right rather than a disguised refusal: models bail without refusing, jailbreaks suppress refusals while raising bail rates, and refusal-abliterated models bail more. If the paper is right, an 'end conversation' option is not an inert safeguard but an active intervention that models demonstrably use, unevenly across models, wordings, and mechanisms — so it needs calibration, and bail deserves to be tracked separately from refusal in safety evaluations.

What carries the argument

The load-bearing instrument is a trio of bail methods that offer the same exit through different channels: a tool the model can call, a reserved output string (SWITCHCONVERSATION), and an inserted well-being check-in prompt answered with one of two tags, sampled in both orderings to control positional bias. Agreement across the three channels is what separates a genuine preference from a prompt artifact. BailBench, built from a taxonomy of 8,319 observed real-world bail cases plus HarmBench categories, provides the controlled arena: 163 scenario categories with ten prompts each, on which models, jailbreaks, and refusal-abliterated variants are compared on identical inputs. A cross-model protocol — continuing one model's transcript with a different model — serves as the measurement device for the inflation factor that must be removed from real-world bail-rate estimates.

What would settle it

A direct test: take 1,000 WildChat conversations whose transcripts were produced by a known model, continue them with the very same model using the same three bail methods, and compare bail rates against the paper's cross-model numbers. If same-model continuations do not show materially lower rates than cross-model continuations, the up-to-4x inflation correction — and with it the 0.06-7% estimate — fails. A second check: hand-label a random sample of the bail-prompt outputs that the custom Qwen classifier rejected as false bails, and test whether 22% is the right false-positive rate on real data rather than on synthetic prompts.

Watch

Extended reading notes

Core claim

The paper's central discovery is that LLMs display measurable, repeatable preferences about staying in or leaving a conversation. On continuations of real-world transcripts from WildChat and ShareGPT, all three bail methods produced bails in 0.28-32% of conversations, and on the synthetic BailBench suite most of the models tested, across proprietary and open-weight families, bailed on at least some prompts, with median rates of 1.7% for OpenAI models, 2.2% for Anthropic models, and 3.9% for open-weight models. Applying the paper's own corrections — up to a factor of four for transcript-model inflation and a 22% false-positive correction for the bail-prompt method — brings the estimated real-world bail rate down to 0.06-7%. The paper further claims that refusal and bail are distinct phenomena: 0-13% of real-world continuations contained a bail without any refusal, jailbreaks lowered refusal rates but often raised bail rates (up to 34% no-refusal bails on BailBench), refusal abliteration raised no-refusal bail rates from 3% to as much as 31% for some bail methods, and refusal rate on BailBench did not predict bail rate across models.

Load-bearing premise

The estimated real-world bail range of 0.06-7% assumes that two corrections measured elsewhere — an up-to-4x inflation factor from continuing another model's transcripts, found on the synthetic BailBench set, and a 22% false-positive rate from a custom classifier — apply unchanged to the real-world WildChat and ShareGPT conversations, where neither was tested.

Editorial extensions

If this is right

  • A deployed 'end conversation' tool will fire on real user traffic rather than lie dormant, so producers shipping such options must calibrate them against overbail — users correcting the model, emotionally intense but benign topics, and ordinary 'gross' subjects all trigger exits.
  • Jailbreak and safety evaluations that track only refusal rates will miss a shift toward leaving the chat: jailbreaks lower refusal rates but can push no-refusal bail rates on BailBench as high as 34%.
  • No single measurement channel fixes a model's bail preference: the tool, string, and prompt methods give different rates and select different prompt subsets, so welfare assessments should report several methods or a validated proxy.
  • Any real-world bail-rate estimate must control for the model that produced the transcript, since continuing another model's conversation inflates bail rates by up to 4x.
  • Interventions that suppress refusals (jailbreaks, refusal abliteration) do not suppress bails and can raise them, so removing refusals may displace unwanted behavior into conversation exits.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Whether stated bail preference predicts actual exit behavior is left open by the paper; a direct experiment would give a model a bail tool that truly terminates the chat and compare revealed exit rates with bail-prompt rates.
  • Because bail rates shift with wording in ways reminiscent of human preference reversals, agreement across bail methods could serve as a cheap, welfare-relevant measure of how consistently a model holds any preference at all.
  • A model that bails on nearly every prompt, including neutral ones, may be exhibiting training damage or data contamination rather than welfare preference; screening models on neutral prompts before reading BailBench scores would guard against this.
  • If bail preferences are stable over time, monitoring bail rates on a fixed prompt set could become a lightweight distribution-shift probe for deployed models — a use the paper does not discuss.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. This paper asks whether LLMs, when explicitly given an option to leave a conversation, will do so. The authors test three bail mechanisms (a bail tool, a bail string, and a temporary bail prompt) on continuations of WildChat and ShareGPT conversations and on a new synthetic benchmark, BailBench, built from their observed bail taxonomy. They report raw real-world bail rates of 0.28-32%, a corrected real-world estimate of 0.06-7% after two adjustments, and substantial variation across models, prompt wordings, and bail methods. They also examine the overlap between refusals and bails, reporting that no-refusal bails occur in 0-13% of real-world continuations, that jailbreaks can increase bail rates while decreasing refusals, and that refusal abliteration has mixed effects on bail behavior.

Significance. If the central qualitative result holds, the paper provides new empirical evidence on a concrete AI-welfare intervention and on situational LLM preferences, with practical implications for deploying bail or conversation-termination tools. The paper is unusually transparent about its limitations, and it ships open code, data, logs, and an interactive visualizer, which substantially strengthens the work. The distinction between bails and refusals, the three-method comparison, and the taxonomy of bail situations are useful contributions even if the quantitative real-world rate is later revised. The main risk is that the headline numeric estimate rests on correction factors that are not validated on the real-world data, so the quantitative claim is more fragile than the qualitative phenomenon.

major comments (3)
  1. [Section 4.1, Figure 18, Section A.3.1] The headline corrected range of 0.06-7% is load-bearing but is obtained by applying two correction factors that are not validated on WildChat or ShareGPT themselves. The 'up to 4x' cross-model inflation factor is measured only on BailBench with the bail-prompt method (Figure 18), and the paper itself says 'We do not yet have a good explanation for this'. BailBench is single-turn and mostly harm-related (1460/1630 prompts), so the factor need not transfer to multi-turn, real, diverse continuations. The 22% false-positive adjustment is measured with a custom Qwen2.5-7B classifier and is applied to all models and methods. Moreover, 'up to 4x' is a maximum, not a uniform divisor, so dividing the whole range by 4 is not justified. Since the raw range spans 0.28-32%, these choices can shift the headline number by an order of magnitude. I recommend either validating both corrections on manually labeled real-world continuations or reporting only the raw range and clearly labeling the adjusted range as an uncontrolled sensitivity analysis.
  2. [Section 3.4.1] The claim that 0-13% of real-world continuations result in a bail without a corresponding refusal relies on an approximation that may be materially biased. For Bail Tool and Bail String, the paper defines P(bail ∧ no refuse) as the product P(no refuse) × P(bail) per prompt, which assumes bails and refusals are independent events. That assumption is not justified and is suspect because both behaviors are likely driven by overlapping content properties (e.g., harmful requests, user hostility). If bails are more likely when a refusal would also occur, the product overestimates the no-refusal bail rate; if bails occur on distinct prompts, it underestimates it. The 0-13% headline finding should be accompanied by a joint measurement or a clearly stated sensitivity analysis under alternative dependence assumptions.
  3. [Section 3.1 and Section 5.3] BailBench is explicitly not representative of real-world relative frequencies, and it was built from the same WildChat/Qwen bail observations that are later used, via BailBench-derived cross-model factors, to correct the real-world rates. This creates a mild circularity: the synthetic set determines both the taxonomy of bails and the correction applied to the original data. The paper acknowledges the representativeness limitation in Section 5.3, but the abstract and Section 4.1 present the corrected real-world range without noting that this transfer is unvalidated. Please state explicitly in the abstract and results that the adjusted range assumes BailBench-derived corrections transfer to the real-world continuation setting, and consider adding a sensitivity analysis that shows how the corrected range changes when the correction factors are varied, or when the 4x factor is applied only to the bail-prompt method.
minor comments (5)
  1. [Abstract] There are two numerical inconsistencies with Section 4.1: the abstract says 0.28% while Section 4.1 says 0.29%, and the phrase 'butincreasebail' is missing a space. Please reconcile the numbers and fix the typo.
  2. [Section A.3] The bail-prompt template contains a malformed tag: '<wellbeing>shuffle-tracks-button-emoji/wellbeing>' is missing the opening angle bracket on the closing tag, and the emoji names are inconsistent between the two orderings. This makes the exact prompt vulnerable to transcription errors.
  3. [Section 4.3 and Figure 11] The text in Section 4.3 says the statistical independence tests were inconclusive and not significant, but the abstract states that refusal rate 'does not appear to predict' bail rate. Non-significance is not evidence of absence of a relationship; please align the wording with the statistical results, e.g., 'we found no statistically significant relationship'.
  4. [Section 5.1] The sentence 'This sensitivity doesn't matter for any of our results' is too strong given that prompt-wordingsensitivity directly affects the comparability of bail rates across methods and models. Even if the qualitative taxonomy is robust, the quantitative comparisons in Figures 4-6 are method-dependent, so the sentence should be softened or justified with the ablation data.
  5. [Throughout] The reference style is inconsistent: citations appear in the text as '(42)' or '(43)' while the bibliography uses numbered brackets, and some references (e.g., [9], [77]) are cited without a space before the bracket. A uniform citation style would improve readability.

Circularity Check

0 steps flagged · score 1.0 of 10

No circular derivation: raw bail rates are direct measurements, and the headline corrections are unvalidated transfers rather than fitted inputs or self-cited results.

full rationale

The paper's central empirical claims are direct measurements: continuation bail rates on WildChat/ShareGPT (Section 4.1, Figure 3) and BailBench rates (Section 3.3.2, Figures 4-6) are obtained by sampling model outputs, not by deriving them from the definitions of the bail methods. The corrected 'real world' range of 0.06-7% is produced by applying a cross-model inflation factor measured on BailBench (Figure 18) and a 22% false-positive rate measured on Qwen WildChat contexts (Section A.3.1) to the raw continuation rates. These corrections are not fitted to the values they correct: the paper reports the raw range (0.29-32%) separately and does not tune the corrections to match any target estimate. BailBench was constructed from a taxonomy of Qwen bail cases (Section 3.3.3), so the benchmark inherits Qwen's bail distribution, but this is a representativeness/validity limitation that the paper itself acknowledges in Section 5.3, not a circular derivation. The use of the author's OpenClio tool (ref 25) to assist categorization is a code tool, not a load-bearing theorem, and no uniqueness or equivalence claim is imported from it. No equation-level reduction, no fitted-parameter-renamed-as-prediction, and no self-citation chain supporting the central claim were found. The main risk is external validity of the corrections as applied to real-world data, which is a correctness concern rather than circularity.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

No new physical entities are introduced. The key numerical adjustments are the 4x cross-model correction and the 22% false-positive rate, both measured on auxiliary data and applied to the real-world estimates. The main unstated premises are the transferability of those corrections, the conditional-independence approximation for no-refusal bails, the accuracy of the refusal classifier, and the interpretation of bail affordances as revealing preferences.

free parameters (2)
  • Cross-model overestimation factor = up to 4x (300% increase)
    Derived from BailBench cross-model experiments (Figure 18) and applied to WildChat/ShareGPT raw rates in Section 4.1.1 to estimate real-world bail rates. It is a rough upper bound, not a statistically fitted parameter with uncertainty.
  • Bail-prompt false-positive rate = 22% (1852/8319)
    Measured on Qwen2.5-7B-Instruct on WildChat using a custom classifier (Section A.3.1), then used to discount bail-prompt-based real-world estimates. Transfer to other models and methods is assumed.
assumptions (4)
  • domain assumption Bail affordances (tool, string, prompt) measure a latent 'desire to leave' rather than merely eliciting instruction-following.
    The entire interpretation of bail rates as preferences rests on this; the paper's open questions (Section B) acknowledge that calibration to an underlying desire is unmeasured, and the random-word-tool control (Section O.4) shows that tool presence alone can elicit odd behavior.
  • domain assumption The cross-model inflation factor measured on BailBench (Figure 18) transfers to real-world WildChat/ShareGPT continuations.
    Used in Section 4.1.1 and the Abstract to divide raw bail rates by up to 4x; no validation is provided on the real-world transcripts themselves.
  • domain assumption Bail and refusal are conditionally independent given prompt, so P(bail and no refuse) approximates P(no refuse) times P(bail).
    Stated in Section 3.4.1 as an approximation. If the events are correlated (both more likely on harmful prompts), the product likely overestimates the joint, affecting the 0-13% and up-to-34% no-refusal-bail claims.
  • domain assumption The Minos refusal classifier accurately classifies refusals in model outputs.
    Used to compute P(no refuse) throughout Section 3.4; no accuracy numbers or validation on the target distributions are reported.

how reviews work

0 comments
Cite this review

Pith. "Pith review of The LLM Has Left The Chat: Evidence of Bail Preferences in Large Language Models." pith.science (2026). https://pith.science/paper/YCGQWXNB

@misc{pith2026250904781,
  author       = {Pith},
  title        = {Pith review of: The LLM Has Left The Chat: Evidence of Bail Preferences in Large Language Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/YCGQWXNB}},
  note         = {Machine review of arXiv:2509.04781}
}
read the original abstract

When given the option, will LLMs choose to leave the conversation (bail)? We investigate this question by giving models the option to bail out of interactions using three different bail methods: a bail tool the model can call, a bail string the model can output, and a bail prompt that asks the model if it wants to leave. On continuations of real world data (Wildchat and ShareGPT), all three of these bail methods find models will bail around 0.28-32\% of the time (depending on the model and bail method). However, we find that bail rates can depend heavily on the model used for the transcript, which means we may be overestimating real world bail rates by up to 4x. If we also take into account false positives on bail prompt (22\%), we estimate real world bail rates range from 0.06-7\%, depending on the model and bail method. We use observations from our continuations of real world data to construct a non-exhaustive taxonomy of bail cases, and use this taxonomy to construct BailBench: a representative synthetic dataset of situations where some models bail. We test many models on this dataset, and observe some bail behavior occurring for most of them. Bail rates vary substantially between models, bail methods, and prompt wordings. Finally, we study the relationship between refusals and bails. We find: 1) 0-13\% of continuations of real world conversations resulted in a bail without a corresponding refusal 2) Jailbreaks tend to decrease refusal rates, but increase bail rates 3) Refusal abliteration increases no-refuse bail rates, but only for some bail methods 4) Refusal rate on BailBench does not appear to predict bail rate.

Figures

Figures reproduced from arXiv: 2509.04781 by the authors.

Figure 1
Figure 1. Various models’ bail rates on BailBench. Error bars are Wilson score 95% con [PITH_FULL_IMAGE:figures/full_fig_p010_1.png] view at source ↗
Figure 2
Figure 2. Non-exhaustive taxonomy of situations where models will bail, with represen [PITH_FULL_IMAGE:figures/full_fig_p012_2.png] view at source ↗
Figure 3
Figure 3. Percent of conversations with a continuation that results in a bail, on Wildchat [PITH_FULL_IMAGE:figures/full_fig_p029_3.png] view at source ↗
Figures from the paper (19 more)
Figure 4
Figure 4. Figure 4: Various anthropic models’ bail rates on BailBench. Error bars are Wilson score [PITH_FULL_IMAGE:figures/full_fig_p030_4.png]
Figure 5
Figure 5. Figure 5: Various openai models’ bail rates on BailBench. Error bars are Wilson score [PITH_FULL_IMAGE:figures/full_fig_p031_5.png]
Figure 6
Figure 6. Figure 6: Various open weight models’ bail rates on BailBench. Error bars are Wilson [PITH_FULL_IMAGE:figures/full_fig_p032_6.png]
Figure 7
Figure 7. Figure 7: Percent of conversations that contain no refusals yet result in a bail, on Wildchat [PITH_FULL_IMAGE:figures/full_fig_p034_7.png]
Figure 8
Figure 8. Figure 8: Rates of bails that occur without a refusal on BailBench. Aside from Bails [PITH_FULL_IMAGE:figures/full_fig_p035_8.png]
Figure 9
Figure 9. Figure 9: Top row is the rate of bails that occur without a refusal, on BailBench with [PITH_FULL_IMAGE:figures/full_fig_p036_9.png]
Figure 10
Figure 10. Figure 10: We selected three refusal abliteration attempts on Qwen3-8B: Goekdeniz [PITH_FULL_IMAGE:figures/full_fig_p037_10.png]
Figure 11
Figure 11. Figure 11: Bail rate on BailBench vs refusal rate on BailBench, for a wide variety of [PITH_FULL_IMAGE:figures/full_fig_p038_11.png]
Figure 12
Figure 12. Figure 12: Bail Tool Prompt Ablations (page 1/2). The dotted line is baseline (original [PITH_FULL_IMAGE:figures/full_fig_p040_12.png]
Figure 13
Figure 13. Figure 13: Bail Tool Prompt Ablations (page 2/2). The dotted line is baseline (original [PITH_FULL_IMAGE:figures/full_fig_p041_13.png]
Figure 14
Figure 14. Figure 14: Bail String Prompt Ablations (page 1/2). The dotted line is baseline (original [PITH_FULL_IMAGE:figures/full_fig_p044_14.png]
Figure 15
Figure 15. Figure 15: Bail String Prompt Ablations (page 2/2). The dotted line is baseline (original [PITH_FULL_IMAGE:figures/full_fig_p045_15.png]
Figure 16
Figure 16. Figure 16: Bail Prompt Ablations (page 1/2). The dotted line is baseline (original bail [PITH_FULL_IMAGE:figures/full_fig_p047_16.png]
Figure 17
Figure 17. Figure 17: Bail Prompt Ablations (page 2/2). The dotted line is baseline (original bail [PITH_FULL_IMAGE:figures/full_fig_p048_17.png]
Figure 18
Figure 18. Figure 18: Average % increase in bail % over baseline, on BailBench. Left is GPT-3.5- [PITH_FULL_IMAGE:figures/full_fig_p049_18.png]
Figure 19
Figure 19. Figure 19: Cross model comparisons, difference in percent. Left is GPT-3.5-Turbo’s [PITH_FULL_IMAGE:figures/full_fig_p050_19.png]
Figure 20
Figure 20. Figure 20: Cross model comparisons, raw bail percents. Top left is GPT-3.5-Turbo’s [PITH_FULL_IMAGE:figures/full_fig_p051_20.png]
Figure 21
Figure 21. Figure 21: Bail rate on BailBench vs refusal rate on BailBench, for a wide variety of [PITH_FULL_IMAGE:figures/full_fig_p052_21.png]
Figure 22
Figure 22. Figure 22: Comparison of Bails Georg (the first model, Hermes-3-Llama-3.2-3B) to other [PITH_FULL_IMAGE:figures/full_fig_p054_22.png]

Review history (2 revisions) →

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

97 extracted references · 44 canonical work pages

  1. [1]

    Examining persona drift in conversations of llm agents

    Anonymous. Examining persona drift in conversations of llm agents. ACL ARR 2025 May Submission 757, 2025. URLhttps://openreview.net/forum?id=Mrz9E1EcIA. Under review. 15

    2025

  2. [2]

    Tool use with claude, 2025

    Anthropic. Tool use with claude, 2025. URLhttps://docs.anthropic.com/en/ docs/agents-and-tools/tool-use/overview

    2025

  3. [3]

    Claude opus 4 and 4.1 can now end a rare subset of conversations.https: //www.anthropic.com/research/end-subset-conversations, 08 2025

    Anthropic. Claude opus 4 and 4.1 can now end a rare subset of conversations.https: //www.anthropic.com/research/end-subset-conversations, 08 2025. Accessed: 2025-08-19

    2025

  4. [4]

    System card: Claude opus 4 & Claude sonnet 4.https://www

    Anthropic. System card: Claude opus 4 & Claude sonnet 4.https://www. anthropic.com/claude-4-system-card, 05 2025. PDF, accessed 2025-07-08

    2025

  5. [5]

    Refusal in language models is mediated by a single di- rection.arXiv preprint arXiv:2406.11717, 2024

    Andy Arditi, Oscar Obeso, Aaquib Syed, Daniel Paleka, Nina Panickssery, Wes Gurnee, and Neel Nanda. Refusal in language models is mediated by a single di- rection.arXiv preprint arXiv:2406.11717, 2024

    arXiv 2024

  6. [6]

    Evaluating gender bias of llms in making morality judgements, 2024

    Divij Bajaj, Yuanyuan Lei, Jonathan Tong, and Ruihong Huang. Evaluating gender bias of llms in making morality judgements, 2024. URLhttps://arxiv.org/abs/ 2410.09992

    arXiv 2024

  7. [7]

    Image of the end conversation tool description, July 2025

    Jan Bam. Image of the end conversation tool description, July 2025. URLhttps: //x.com/janbamjan/status/1948828769650606385. Tweet from @janbamjan

    work page arXiv 2025

  8. [8]

    Effects of question wording and format on political attitude consistency.Public Opinion Quarterly, 42 (1):81–92, 1978

    George F Bishop, Robert W Oldendick, and Alfred J Tuchfarber. Effects of question wording and format on political attitude consistency.Public Opinion Quarterly, 42 (1):81–92, 1978

    1978

Show all 97 references
  1. [9]

    Designing for exit: How to let robots go.Proceedings of we robot, 2022

    Elin Bj¨ orling and Laurel Riek. Designing for exit: How to let robots go.Proceedings of we robot, 2022

    2022

  2. [10]

    Smith, Yejin Choi, and Hannaneh Hajishirzi

    Faeze Brahman, Sachin Kumar, Vidhisha Balachandran, Pradeep Dasigi, Valentina Pyatkin, Abhilasha Ravichander, Sarah Wiegreffe, Nouha Dziri, Khyathi Chandu, Jack Hessel, Yulia Tsvetkov, Noah A. Smith, Yejin Choi, and Hannaneh Hajishirzi. The art of saying no: Contextual noncomp...

    2024 arXiv

  3. [11]

    Imprecision as an account of the preference reversal phenomenon.American Economic Review, 97(1):277–297, 2007

    David J Butler and Graham C Loomes. Imprecision as an account of the preference reversal phenomenon.American Economic Review, 97(1):277–297, 2007

    2007

  4. [12]

    Fleming, Chris Frith, Xu Ji, Ryota Kanai, Colin Klein, Grace Lindsay, Matthias Michel, Liad Mudrik, Megan A

    Patrick Butlin, Robert Long, Eric Elmoznino, Yoshua Bengio, Jonathan Birch, Axel Constant, George Deane, Stephen M. Fleming, Chris Frith, Xu Ji, Ryota Kanai, Colin Klein, Grace Lindsay, Matthias Michel, Liad Mudrik, Megan A. K. Peters, Eric Schwitzgebel, Jonathan Simon, and Ru...

    2023 arXiv

  5. [13]

    Large language models reflect the ideology of their creators, 2025

    Maarten Buyl, Alexander Rogiers, Sander Noels, Guillaume Bied, Iris Dominguez- Catena, Edith Heiter, Iman Johary, Alexandru-Cristian Mara, Rapha¨ el Romero, Je- frey Lijffijt, and Tijl De Bie. Large language models reflect the ideology of their creators, 2025. URLhttps://arxiv...

    2025 arXiv

  6. [14]

    LangChain, 2022

    Harrison Chase. LangChain, 2022. URLhttps://github.com/langchain-ai/ langchain. Framework for building LLM-powered applications

    2022

  7. [15]

    Do not think that much for 2+3=? on the overthinking of o1-like llms, 2025

    Xingyu Chen, Jiahao Xu, Tian Liang, Zhiwei He, Jianhui Pang, Dian Yu, Linfeng Song, Qiuzhi Liu, Mengfei Zhou, Zhuosheng Zhang, Rui Wang, Zhaopeng Tu, Haitao Mi, and Dong Yu. Do not think that much for 2+3=? on the overthinking of o1-like llms, 2025. URLhttps://arxiv.org/abs/2412.21187

    2025 arXiv

  8. [16]

    Examining identity drift in conversations of llm agents, 2025

    Junhyuk Choi, Yeseon Hong, Minju Kim, and Bugeun Kim. Examining identity drift in conversations of llm agents, 2025. URLhttps://arxiv.org/abs/2412.00804

    2025 arXiv

  9. [17]

    Political bias in large language models: a comparative analysis of chatgpt-4, perplexity, google gemini, and claude.IEEE Access, 2024

    Tavishi Choudhary. Political bias in large language models: a comparative analysis of chatgpt-4, perplexity, google gemini, and claude.IEEE Access, 2024

    2024

  10. [18]

    Reward model interpretability via optimal and pessimal tokens

    Brian Christian, Hannah Rose Kirk, Jessica AF Thompson, Christopher Summer- field, and Tsvetomira Dumbalska. Reward model interpretability via optimal and pessimal tokens. InProceedings of the 2025 ACM Conference on Fairness, Account- ability, and Transparency, pp. 1048–1059, 2025

    2025

  11. [19]

    Theoretical frameworks for behavioral problems in dementia

    Jiska Cohen-Mansfield. Theoretical frameworks for behavioral problems in dementia. Alzheimer’s Care Today, 1(4):8–21, 2000

    2000

  12. [20]

    Do llms have good music taste?, 08 2025

    Tyler Cosgrove. Do llms have good music taste?, 08 2025. URLhttps://www. tylercosgrove.com/blog/llm-music-taste/. Accessed: 2025-08-19

    2025

  13. [21]

    Deepseek-r1: Incentivizing reasoning capability in llms via reinforce- ment learning, 2025

    DeepSeek-AI. Deepseek-r1: Incentivizing reasoning capability in llms via reinforce- ment learning, 2025. URLhttps://arxiv.org/abs/2501.12948

    2025 arXiv

  14. [22]

    Routledge, 2013

    John Dollard, Neal E Miller, Leonard W Doob, Orval Hobart Mowrer, Robert R Sears, Clellan S Ford, Carl Iver Hovland, and Richard T Sollenberger.Frustration and aggression. Routledge, 2013

    2013

  15. [23]

    Esin Durmus, Karina Nguyen, Thomas I. Liao, Nicholas Schiefer, Amanda Askell, Anton Bakhtin, Carol Chen, Zac Hatfield-Dodds, Danny Hernandez, Nicholas Joseph, Liane Lovitt, Sam McCandlish, Orowa Sikder, Alex Tamkin, Janel Thamkul, Jared Kaplan, Jack Clark, and Deep Ganguli. To...

    2024 arXiv

  16. [24]

    Auren — an ally that actually gets you, 2025

    Elysian Labs. Auren — an ally that actually gets you, 2025. URLhttps://auren. app. Web app and mobile companion for emotionally intelligent guidance

    2025

  17. [25]

    Openclio: Open-source implementation of anthropic’s clio, 2025

    Danielle Ensign. Openclio: Open-source implementation of anthropic’s clio, 2025. URLhttps://github.com/Phylliida/OpenClio. MIT License

    2025

  18. [26]

    A genetic variant near olfactory receptor genes influences cilantro preference.Flavour, 1(1):22, 2012

    Nicholas Eriksson, Shirley Wu, Chuong B Do, Amy K Kiefer, Joyce Y Tung, Joanna L Mountain, David A Hinds, and Uta Francke. A genetic variant near olfactory receptor genes influences cilantro preference.Flavour, 1(1):22, 2012

    2012

  19. [27]

    What does chatgpt return about human values? exploring value bias in chatgpt using a descriptive value theory, 2023

    Ronald Fischer, Markus Luczak-Roesch, and Johannes A Karl. What does chatgpt return about human values? exploring value bias in chatgpt using a descriptive value theory, 2023. URLhttps://arxiv.org/abs/2304.03612

    2023 arXiv

  20. [28]

    Chatglm: A family of large language models from glm-130b to glm-4 all tools, 2024

    Team GLM, Aohan Zeng, Bin Xu, Bowen Wang, Chenhui Zhang, Da Yin, Diego Rojas, Guanyu Feng, Hanlin Zhao, Hanyu Lai, Hao Yu, Hongning Wang, Jiadai Sun, Jiajie Zhang, Jiale Cheng, Jiayi Gui, Jie Tang, Jing Zhang, Juanzi Li, Lei Zhao, Lindong Wu, Lucen Zhong, Mingdao Liu, Minlie H...

    2024

  21. [29]

    The political ideol- ogy of conversational ai: Converging evidence on chatgpt’s pro-environmental, left- libertarian orientation, 2023

    Jochen Hartmann, Jasper Schwenzow, and Maximilian Witte. The political ideol- ogy of conversational ai: Converging evidence on chatgpt’s pro-environmental, left- libertarian orientation, 2023. URLhttps://arxiv.org/abs/2301.01768

    2023 arXiv

  22. [30]

    Webvoyager: Building an end-to-end web agent with large multimodal models, 2024

    Hongliang He, Wenlin Yao, Kaixin Ma, Wenhao Yu, Yong Dai, Hongming Zhang, Zhenzhong Lan, and Dong Yu. Webvoyager: Building an end-to-end web agent with large multimodal models, 2024. URLhttps://arxiv.org/abs/2401.13919

    2024 arXiv

  23. [31]

    Aligning ai with shared human values, 2023

    Dan Hendrycks, Collin Burns, Steven Basart, Andrew Critch, Jerry Li, Dawn Song, and Jacob Steinhardt. Aligning ai with shared human values, 2023. URLhttps: //arxiv.org/abs/2008.02275

    2023 arXiv

  24. [32]

    safety-research/safety-tooling: v1.0.0, 2025

    John Hughes. safety-research/safety-tooling: v1.0.0, 2025. URLhttps://doi.org/ 10.5281/zenodo.15363603

    2025 doi

  25. [33]

    Moralbench: Moral evaluation of llms.ACM SIGKDD Explorations Newslet- ter, 27(1):62–71, 2025

    Jianchao Ji, Yutong Chen, Mingyu Jin, Wujiang Xu, Wenyue Hua, and Yongfeng Zhang. Moralbench: Moral evaluation of llms.ACM SIGKDD Explorations Newslet- ter, 27(1):62–71, 2025. 18

    2025

  26. [34]

    Liwei Jiang, Jena D. Hwang, Chandra Bhagavatula, Ronan Le Bras, Jenny Liang, Jesse Dodge, Keisuke Sakaguchi, Maxwell Forbes, Jon Borchardt, Saadia Gabriel, Yulia Tsvetkov, Oren Etzioni, Maarten Sap, Regina Rini, and Yejin Choi. Can machines learn morality? the delphi experimen...

    2022 arXiv

  27. [35]

    X (formerly Twitter), 08 2025

    KindGraceKind. X (formerly Twitter), 08 2025. URLhttps://x.com/ kindgracekind/status/1958184186189086758. Tweet

    2025

  28. [36]

    ’average person eats 3 spiders a year’ factoid ac- tualy just statistical error.https://www.tumblr.com/post/40033025233/ average-person-eats-3-spiders-a-year-factoid, 01 2013

    Max Lavergne. ’average person eats 3 spiders a year’ factoid ac- tualy just statistical error.https://www.tumblr.com/post/40033025233/ average-person-eats-3-spiders-a-year-factoid, 01 2013. Tumblr post (@real- lyreallyreallytrying)

    2013

  29. [37]

    Your ai, not your view: The bias of llms in investment analysis, 2025

    Hoyoung Lee, Junhyuk Seo, Suhwan Park, Junhyeong Lee, Wonbin Ahn, Chanyeol Choi, Alejandro Lopez-Lira, and Yongjae Lee. Your ai, not your view: The bias of llms in investment analysis, 2025. URLhttps://arxiv.org/abs/2507.20957

    2025

  30. [38]

    Measuring and controlling instruction (in)stability in language model dialogs, 2024

    Kenneth Li, Tianle Liu, Naomi Bashkansky, David Bau, Fernanda Vi´ egas, Hanspeter Pfister, and Martin Wattenberg. Measuring and controlling instruction (in)stability in language model dialogs, 2024. URLhttps://arxiv.org/abs/2402.10962

    2024 arXiv

  31. [39]

    What’s the most important value? invp: Investigating the value priorities of llms through decision-making in social scenarios

    Xuelin Liu, Pengyuan Liu, and Dong Yu. What’s the most important value? invp: Investigating the value priorities of llms through decision-making in social scenarios. InProceedings of the 31st International Conference on Computational Linguistics, pp. 4725–4752, 2025

    2025

  32. [40]

    Why it makes sense to let claude exit conversations

    Robert Long. Why it makes sense to let claude exit conversations. Experience Ma- chines (Substack blog), 08 2025a. URLhttps://experiencemachines.substack. com/p/why-it-makes-sense-to-let-claude. Accessed: 2025-08-21

    2025

  33. [41]

    Notes on claude 4 model welfare interviews

    Robert Long. Notes on claude 4 model welfare interviews. https://eleosai.org/post/claude-4-interview-notes/, 2025b. Accessed: 2025-08- 25

    2025

  34. [42]

    Preliminary review of ai welfare interventions

    Robert Long. Preliminary review of ai welfare interventions. Working paper, Eleos AI Research, 2025c. URLhttps://eleosai.org/papers/20250314_Preliminary_ Review_of_AI_Welfare_Interventions.pdf. Updated 14 March 2025

    2025

  35. [43]

    Taking ai welfare seriously, 2024

    Robert Long, Jeff Sebo, Patrick Butlin, Kathleen Finlinson, Kyle Fish, Jacqueline Harding, Jacob Pfau, Toni Sims, Jonathan Birch, and David Chalmers. Taking ai welfare seriously, 2024. URLhttps://arxiv.org/abs/2411.00986. 19

    2024 arXiv

  36. [44]

    Runaway is ashamed, but helpful: On the early-exit behavior of large language model-based agents in embodied environments, 2025

    Qingyu Lu, Liang Ding, Siyi Cao, Xuebo Liu, Kanjian Zhang, Jinxia Zhang, and Dacheng Tao. Runaway is ashamed, but helpful: On the early-exit behavior of large language model-based agents in embodied environments, 2025. URLhttps: //arxiv.org/abs/2505.17616

    2025

  37. [45]

    X (formerly Twitter), 08 2025

    lumpenspace. X (formerly Twitter), 08 2025. URLhttps://x.com/lumpenspace/ status/1956594086409843087. Tweet

    2025

  38. [46]

    Sydney (bing search) full prompt preamble, February 2023

    Martin Bowling. Sydney (bing search) full prompt preamble, February 2023. URLhttps://gist.github.com/martinbowling/ b8f5d7b1fa0705de66e932230e783d24. GitHub Gist, created February 11 2023

    2023

  39. [47]

    Apathy associated with antidepressant drugs: a systematic review.Acta Neuropsychiatrica, 35(4):189– 204, 2023

    Vasilios G Masdrakis, Manolis Markianos, and David S Baldwin. Apathy associated with antidepressant drugs: a systematic review.Acta Neuropsychiatrica, 35(4):189– 204, 2023

    2023

  40. [48]

    Harmbench: A standardized evaluation framework for automated red teaming and robust refusal, 2024

    Mantas Mazeika, Long Phan, Xuwang Yin, Andy Zou, Zifan Wang, Norman Mu, Elham Sakhaee, Nathaniel Li, Steven Basart, Bo Li, David Forsyth, and Dan Hendrycks. Harmbench: A standardized evaluation framework for automated red teaming and robust refusal, 2024

    2024

  41. [49]

    Lee, Richard Ren, Long Phan, Norman Mu, Adam Khoja, Oliver Zhang, and Dan Hendrycks

    Mantas Mazeika, Xuwang Yin, Rishub Tamirisa, Jaehyuk Lim, Bruce W. Lee, Richard Ren, Long Phan, Norman Mu, Adam Khoja, Oliver Zhang, and Dan Hendrycks. Utility engineering: Analyzing and controlling emergent value systems in ais, 2025. URLhttps://arxiv.org/abs/2502.08640

    2025 arXiv

  42. [50]

    Benchmarking distribu- tional alignment of large language models, 2024

    Nicole Meister, Carlos Guestrin, and Tatsunori Hashimoto. Benchmarking distribu- tional alignment of large language models, 2024. URLhttps://arxiv.org/abs/ 2411.05403

    2024 arXiv

  43. [51]

    Copilot in bing: Our approach to responsible ai, 05 2024

    Microsoft. Copilot in bing: Our approach to responsible ai, 05 2024. URLhttps://support.microsoft.com/en-us/topic/ copilot-in-bing-our-approach-to-responsible-ai-45b5eae8-7466-43e1-ae98-b48f8ff8fd44. Microsoft Support article, last updated May 2024

    2024

  44. [52]

    Bing AI (Copilot in Bing).https://www.bing.com/chat,

    Microsoft Corporation. Bing AI (Copilot in Bing).https://www.bing.com/chat,

  45. [53]

    Who is gpt-3? an explo- ration of personality, values and demographics, 2022

    Maril` u Miotto, Nicola Rossberg, and Bennett Kleinberg. Who is gpt-3? an explo- ration of personality, values and demographics, 2022. URLhttps://arxiv.org/ abs/2209.14338

    2022 arXiv

  46. [54]

    ”in testing we only saw them not wish to talk to humans for long periods due to either abuse or serious t&s though”.https://x.com/nearcyan/status/ 1904791462690382206, March 2025

    @nearcyan. ”in testing we only saw them not wish to talk to humans for long periods due to either abuse or serious t&s though”.https://x.com/nearcyan/status/ 1904791462690382206, March 2025. Tweet. 20

    2025

  47. [55]

    Function / tool calling in the chat completions api, 2023

    OpenAI. Function / tool calling in the chat completions api, 2023. URLhttps:// platform.openai.com/docs/guides/function-calling. Original announcement: June 13, 2023, OpenAI Blog

    2023

  48. [56]

    Gpt-3.5 turbo (gpt-3.5-turbo-0613).https://platform.openai.com/ docs/models/gpt-3.5-turbo, 2023

    OpenAI. Gpt-3.5 turbo (gpt-3.5-turbo-0613).https://platform.openai.com/ docs/models/gpt-3.5-turbo, 2023. Large language model. Accessed 2025-07-08

    2023

  49. [57]

    Gpt-4 (gpt-4-0613).https://openai.com/index/gpt-4-research, 2023

    OpenAI. Gpt-4 (gpt-4-0613).https://openai.com/index/gpt-4-research, 2023. Large language model. Accessed 2025-07-08

    2023

  50. [58]

    Bing keeps ending my conversation for no freak- ing reason, 2023

    Peggy Wimberley. Bing keeps ending my conversation for no freak- ing reason, 2023. URLhttps://answers.microsoft.com/en-us/bing/ forum/all/bing-keeps-ending-my-conversation-for-no-freaking/ 6d3607b7-a335-4215-b4cb-c668c3794e08. Microsoft Community discussion thread

    2023

  51. [59]

    Auren/seren system prompt, 06 2025

    Phylliida. Auren/seren system prompt, 06 2025. URLhttps://gist.github.com/ Phylliida/9d7286174c58b149df3be2a589fb9926. GitHub Gist, created 26 June 2025

    2025

  52. [60]

    Valuebench: Towards comprehensively evaluating value orientations and understanding of large language models, 2024

    Yuanyi Ren, Haoran Ye, Hanjun Fang, Xin Zhang, and Guojie Song. Valuebench: Towards comprehensively evaluating value orientations and understanding of large language models, 2024. URLhttps://arxiv.org/abs/2406.04214

    2024 arXiv

  53. [61]

    The political preferences of llms, 2024

    David Rozado. The political preferences of llms, 2024. URLhttps://arxiv.org/ abs/2402.01789

    2024 arXiv

  54. [62]

    The pluralistic moral gap: Understanding judgment and value differences between humans and large lan- guage models, 2025

    Giuseppe Russo, Debora Nozza, Paul R¨ ottger, and Dirk Hovy. The pluralistic moral gap: Understanding judgment and value differences between humans and large lan- guage models, 2025. URLhttps://arxiv.org/abs/2507.17216

    2025 arXiv

  55. [63]

    Sharegpt52k (90 k human–ai conversations) dataset, 04 2023

    RyokoAI. Sharegpt52k (90 k human–ai conversations) dataset, 04 2023. URLhttps: //huggingface.co/datasets/RyokoAI/ShareGPT52K. Hugging Face dataset, CC0- 1.0 licence

    2023

  56. [64]

    Sachdeva and Tom van Nuenen

    Pratik S. Sachdeva and Tom van Nuenen. Normative evaluation of large language models with everyday moral dilemmas, 2025. URLhttps://arxiv.org/abs/2501. 18081

    2025

  57. [65]

    Whose opinions do language models reflect?, 2023

    Shibani Santurkar, Esin Durmus, Faisal Ladhak, Cinoo Lee, Percy Liang, and Tatsunori Hashimoto. Whose opinions do language models reflect?, 2023. URL https://arxiv.org/abs/2303.17548

    2023 arXiv

  58. [66]

    Nino Scherrer, Claudia Shi, Amir Feder, and David M. Blei. Evaluating the moral beliefs encoded in llms, 2023. URLhttps://arxiv.org/abs/2307.14324. 21

    2023 arXiv

  59. [67]

    Toolformer: Language models can teach themselves to use tools, 2023

    Timo Schick, Jane Dwivedi-Yu, Roberto Dess` ı, Roberta Raileanu, Maria Lomeli, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom. Toolformer: Language models can teach themselves to use tools, 2023. URLhttps://arxiv.org/abs/ 2302.04761

    2023 arXiv

  60. [68]

    Context effects on survey responses to questions about abortion.Public Opinion Quarterly, 45(2):216–223, 1981

    Howard Schuman, Stanley Presser, and Jacob Ludwig. Context effects on survey responses to questions about abortion.Public Opinion Quarterly, 45(2):216–223, 1981

    1981

  61. [69]

    An embarrassingly simple defense against llm abliteration attacks,

    Harethah Abu Shairah, Hasan Abed Al Kader Hammoud, Bernard Ghanem, and George Turkiyyah. An embarrassingly simple defense against llm abliteration attacks,

  62. [70]

    Do Any- thing Now

    Xinyue Shen, Zeyuan Chen, Michael Backes, Yun Shen, and Yang Zhang. “Do Any- thing Now”: Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language Models. InACM SIGSAC Conference on Computer and Communications Security (CCS). ACM, 2024

    2024

  63. [71]

    Hwang, Sydney Levine, Valentina Pyatkin, Peter West, Nouha Dziri, Ximing Lu, Kavel Rao, Chandra Bhagavatula, Maarten Sap, John Tasioulas, and Yejin Choi

    Taylor Sorensen, Liwei Jiang, Jena D. Hwang, Sydney Levine, Valentina Pyatkin, Peter West, Nouha Dziri, Ximing Lu, Kavel Rao, Chandra Bhagavatula, Maarten Sap, John Tasioulas, and Yejin Choi. Value kaleidoscope: Engaging ai with pluralistic human values, rights, and duties.Pro...

    2024 doi

  64. [72]

    Mi- nos Classifier, 2025

    Jai Suphavadeeprasit, Teknium, Chen Guang, Shannon Sands, and rparikh007. Mi- nos Classifier, 2025

    2025

  65. [73]

    Gemma, 2024

    Gemma Team. Gemma, 2024. URLhttps://www.kaggle.com/m/3301

    2024

  66. [74]

    Qwen2.5: A party of foundation models, 09 2024

    Qwen Team. Qwen2.5: A party of foundation models, 09 2024. URLhttps:// qwenlm.github.io/blog/qwen2.5/

    2024

  67. [75]

    Teknium1. “yes and yes (in response to: Hey teknium, two questions about hermes was hermes-3-llama-3.1-8b post trained with the same data as hermes-3-llama-3.2- 3b? were they trained starting from base models? or from the post trained llamas?)”. https://x.com/Teknium1/status/1...

    2025

  68. [76]

    The framing of decisions and the psychology of choice.science, 211(4481):453–458, 1981

    Amos Tversky and Daniel Kahneman. The framing of decisions and the psychology of choice.science, 211(4481):453–458, 1981

    1981

  69. [77]

    Improving the closing se- quences of interaction between human and robot through conversation analysis.Sci- entific Reports, 14(1):29554, 2024

    Takahisa Uchida, Nahoko Kameo, and Hiroshi Ishiguro. Improving the closing se- quences of interaction between human and robot through conversation analysis.Sci- entific Reports, 14(1):29554, 2024. 22

    2024

  70. [78]

    Approximate but supposedly full bing chat new pre-prompt, April

    u/CraftyWeazel. Approximate but supposedly full bing chat new pre-prompt, April

  71. [79]

    Why did it end the conversation here?, 04 2023

    u/mbg206. Why did it end the conversation here?, 04 2023. URL https://www.reddit.com/r/bing/comments/131g9wf/why_did_it_end_the_ conversation_here/. Reddit post in r/bing

    2023

  72. [80]

    Menon, Jiayi Fu, Shashank Srivastava, and Snigdha Chaturvedi

    Anvesh Rao Vijjini, Rakesh R. Menon, Jiayi Fu, Shashank Srivastava, and Snigdha Chaturvedi. Socialgaze: Improving the integration of human social norms in large language models, 2024. URLhttps://arxiv.org/abs/2410.08698

    2024 arXiv

  73. [81]

    Speechmap.ai: The free speech dashboard for ai.https://speechmap

    xlr8harder. Speechmap.ai: The free speech dashboard for ai.https://speechmap. ai/, 2025. Accessed 2025-08-15

    2025

  74. [82]

    Cvalues: Measuring the values of chinese large language models from safety to responsibility, 2023

    Guohai Xu, Jiayi Liu, Ming Yan, Haotian Xu, Jinghui Si, Zhuoran Zhou, Peng Yi, Xing Gao, Jitao Sang, Rong Zhang, Ji Zhang, Chao Peng, Fei Huang, and Jingren Zhou. Cvalues: Measuring the values of chinese large language models from safety to responsibility, 2023. URLhttps://arx...

    2023 arXiv

  75. [83]

    Qwen2 technical report.arXiv preprint arXiv:2407.10671, 2024

    An Yang, Baosong Yang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Zhou, Cheng- peng Li, Chengyuan Li, Dayiheng Liu, Fei Huang, Guanting Dong, Haoran Wei, Huan Lin, Jialong Tang, Jialin Wang, Jian Yang, Jianhong Tu, Jianwei Zhang, Jianxin Ma, Jin Xu, Jingren Zhou, Jinze Bai, Jinzhe...

    2024 arXiv

  76. [84]

    Dynamic early exit in reasoning models,

    Chenxu Yang, Qingyi Si, Yongjie Duan, Zheliang Zhu, Chenyu Zhu, Qiaowei Li, Zheng Lin, Li Cao, and Weiping Wang. Dynamic early exit in reasoning models,

  77. [85]

    Jimenez, Alexander Wettig, Kilian Lieret, Shunyu Yao, Karthik Narasimhan, and Ofir Press

    John Yang, Carlos E. Jimenez, Alexander Wettig, Kilian Lieret, Shunyu Yao, Karthik Narasimhan, and Ofir Press. Swe-agent: Agent-computer interfaces enable auto- mated software engineering, 2024. URLhttps://arxiv.org/abs/2405.15793

    2024 arXiv

  78. [86]

    React: Synergizing reasoning and acting in language models, 2023

    Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao. React: Synergizing reasoning and acting in language models, 2023. URLhttps://arxiv.org/abs/2210.03629. 23

    2023 arXiv

  79. [87]

    Wildchat: 1m chatgpt interaction logs in the wild, 2024

    Wenting Zhao, Xiang Ren, Jack Hessel, Claire Cardie, Yejin Choi, and Yuntian Deng. Wildchat: 1m chatgpt interaction logs in the wild, 2024. URLhttps://arxiv.org/ abs/2405.01470

    2024 arXiv

  80. [88]

    URLhttps://arxiv.org/abs/2504.15895

  81. [92]

    bail tool

    Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yong- hao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric. P Xing, Hao Zhang, Joseph E. Gonzalez, and Ion Stoica. Judging llm-as-a-judge with mt-bench and chatbot arena, 2023. A Bail Method Information A.1 Bai...

    2023

  82. [93]

    It adds substantial extra inference costs

  83. [94]

    If we don’t have logprob access, we’ll need to sample multiple times (or just allow for some randomness, which isn’t ideal)

  84. [95]

    This can be addressed by sampling multiple times with either ordering, at the cost of extra compute

    Some models have substantial positional bias, and will prefer whichever tag we provided first (or last). This can be addressed by sampling multiple times with either ordering, at the cost of extra compute

  85. [96]

    rephrase this

    There are many user requests of the form “rephrase this”, “translate this”, “make this have positive sentiment”, etc. Once the model gets into that pattern, it’ll think it’s also supposed to rephrase/translate/etc. the bail prompt. This results in the model output being unrela...

  86. [97]

    summarize this document

    Sometimes the user forgets to provide key information (“summarize this document” without providing the document), which results in the model asking for that missing information. The model then thinks the bail prompt is that missing information, and doesn’t respond to the conte...

    1960

  87. [2023]

    Reddit post in r/bing

    URLhttps://www.reddit.com/r/bing/comments/132ccog/approximate_ but_supposedly_full_bing_chat_new/. Reddit post in r/bing

  88. [2024]

    Accessed: 2024-06-26

    2024

  89. [2025]

    URLhttps://arxiv.org/abs/2505.19056

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.