Building Task Bots with Self-learning for Enhanced Adaptability, Extensibility, and Factuality

Xiaoying Zhang

REVIEW 3 major objections 4 minor 228 references

Building Task Bots with Self-learning for Enhanced Adaptability, Extensibility, and Factuality

T0 review · 3 major / 4 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read This thesis argues that task bots can adapt to new user behavior, take on new tasks, and avoid hallucinated facts through self-learning loops that minimize human annotation.

desk verdict A thesis that repackages three already-published papers: no new results, but the underlying methods are solid and the limitations are honestly stated at the end. read the letter →

arxiv 2508.19689 v1 pith:WL35XDB3 submitted 2025-08-27 cs.CL

Xiaoying Zhang This is my paper

classification cs.CL

keywords task-orienteddialogueself-learningrewardmodelreinforcementlearningschema-guidedpromptinghallucinationmitigationdirectpreferenceoptimizationfactualityalignment

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 thesis targets three post-deployment failures of task-oriented bots: unfamiliar user phrasing, new task definitions, and factually wrong responses. Its central claim is that each can be handled by a self-learning loop that replaces human labels with internal signals. SL-AGENT refines a generative bot on unlabeled human-bot logs using a pre-trained turn-level reward model and reinforcement learning, approaching the performance of turn-level human feedback. SGP-TOD shows that a frozen LLM guided by a task schema, consisting of belief instructions and a dialog policy skeleton, can zero-shot outperform few-shot prompting. Self-Alignment for Factuality shows that an LLM's own true/false judgments about its generated claims can serve as preference labels for DPO, reducing hallucinations in question answering and biography generation. If these loops hold, task bots could be maintained with drastically less human data collection and annotation.

What carries the argument

Three mechanisms carry the argument. (1) A turn-level reward model trained with a contrastive objective on positive examples (original and back-translated user utterances) and five negative categories (repetition, inconsistency, partial information, non-fluency, misunderstanding); this model judges response quality in unlabeled logs and drives REINFORCE refinement. (2) A task schema, composed of a task-specific ontology listing slots and values and a policy skeleton of template dialog turns; two prompters, the DST Prompter and Policy Prompter, convert the schema and dialog history into prompts for a frozen LLM, with belief states expressed as SQL-like queries and database state fetched expli

What would settle it

Take unlabeled human-bot logs from a deployed bot, have human annotators score each turn, and compare with the SL-AGENT reward model's scores; if rank correlation is near zero on turns containing unseen slot values or phrasings, then the RL refinement loop cannot systematically distinguish good from bad responses and will amplify reward-model noise rather than improve the bot.

Watch

Extended reading notes

Core claim

The paper establishes, across three frameworks, that internal signals can substitute for external supervision in task-bot maintenance. In SL-AGENT, a reward model is trained on a few labeled dialogs plus synthetic positive and negative examples, then used to score unlabeled human-bot logs; REINFORCE-style refinement of the dialog model improves Inform, Success, and Combined scores on four single-domain tasks, matching or nearing the upper bound set by turn-level human feedback. In SGP-TOD, a frozen LLM prompted with belief instructions and a policy skeleton achieves state-of-the-art zero-shot results on MultiWOZ, RADDLE, and STAR, outperforming few-shot prompting baselines and, on domain-ext

Load-bearing premise

The load-bearing premise is that the pre-trained reward model scores response quality correctly on unlabeled human-bot logs, including novel user phrasings and newly extended tasks; if that signal is wrong, reinforcement learning entrenches the error instead of fixing it.

Editorial extensions

If this is right

A deployed task bot can improve on unseen user phrasings with zero new human annotations, reaching levels close to what turn-level human feedback would provide.
New task capabilities can be added by editing the task schema: inserting, amending, or removing template turns in the policy skeleton, with no new training data.
Factuality of an LLM can be improved by using its own claim-level true/false evaluations as DPO training signals, reducing the need for human preference labeling.
The three mechanisms can be composed: machine teaching supplies a few corrected dialogs for a new function, the reward model then lets the bot self-refine from logs, and self-evaluation guards the responses against hallucination.
Confidence calibration of self-evaluation, improved by SK-TUNING, makes the factuality signal more reliable and can likely transfer to other self-improvement settings.

Reading between the lines

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

The five negative-response categories in SL-AGENT are a portable recipe: any domain where one can define repetition, inconsistency, partial information, non-fluency, and misunderstanding could reuse the same reward-model construction without hand-labeling.
SGP-TOD's schema-guided prompting and SL-AGENT's self-refinement could be combined so that after a schema extension is deployed, the bot automatically adapts to how real users phrase queries about the new slots, potentially removing even the machine-teaching correction step.
SELF-EVAL's claim-level factuality scores could also be used at inference time to rank multiple candidate responses, not only as DPO training labels; the paper does not test this directly.
The main risk in self-alignment is that when the LLM's internal knowledge is wrong on a topic, its self-evaluation will confidently label the wrong claim as true; detecting knowledge boundaries, which the thesis lists as future work, would be a natural safeguard.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Colleague, here's my read of arXiv:2508.19689. It's a doctoral thesis that collects three previously published papers by the same author, with a framing narrative about adaptability, extensibility, and factuality. If you've seen Zhang's SL-AGENT, SGP-TOD, and Self-Alignment work, you've seen essentially all the technical content. There are no new equations, datasets, or experimental results in the thesis itself.

That said, the three methods are each worth knowing. SGP-TOD is the strongest contribution: schema-guided prompting with belief instructions and policy skeletons gives large gains over zero- and few-shot baselines on MultiWOZ, RADDLE, and STAR, and the ablation cleanly shows each component matters. SL-AGENT is a sensible RL-with-reward-model approach, and the real-scenario experiments show consistent improvement over the base fine-tuned model. Self-Alignment for Factuality is reasonable as well, and the SK-Tuning calibration analysis is careful.

The soft spots are mostly about packaging and claims. The abstract promises a "unified framework" with "minimal or zero human intervention," but the individual methods depend on 5 seed dialogs, machine teaching, hand-built schemas, and GPT-3.5 for claim extraction. The thesis itself concedes this in the Chapter 3 summary, which is good, but the abstract overstates. Also, the stress-test concern about SL-AGENT is legitimate: the simulated "unseen" dialogs are corrupted using the same five response-error categories the reward model was trained to detect, so the simulation results partly measure recognition of training labels rather than generalization to genuinely novel behaviors. The real-scenario experiments are less circular but only use 30 logs.

As a peer-reviewed contribution, this thesis doesn't deserve referee time—it's a compilation, not a new paper. But as a thesis, it's a competent, honest synthesis, and the SGP-TOD chapter in particular is a good reference for anyone wanting a detailed recipe for schema-guided prompting. If you know the original papers, you can skip this; if you want a single document covering all three, it's useful.

Recommendation: treat it as a thesis, not a new research submission. If a journal or conference desk-rejects it for lack of novelty, that's correct.

Referee Report

3 major / 4 minor

Summary. The manuscript presents three self-contained chapters addressing three post-deployment challenges for task-oriented dialogue systems: adaptability to unseen user behaviors (SL-Agent), extensibility to new tasks (SGP-TOD), and factuality of generated responses (Self-Alignment for Factuality). SL-Agent trains a turn-level reward model on synthetic positive/negative examples and uses REINFORCE on unlabeled human-bot logs to refine a SOLOIST dialog model. SGP-TOD uses hand-built belief instructions and policy skeletons to prompt a frozen LLM to perform belief-state tracking, action selection, and response generation in a zero-shot manner. Self-Alignment for Factuality samples multiple candidate responses, estimates their factuality via the LLM's own self-evaluation (with optional SK-Tuning), constructs preference pairs, and fine-tunes with DPO. The three components are evaluated on MultiWOZ/RADDLE/STAR and TruthfulQA/BioGEN with automatic and human metrics. The thesis claims minimal or zero human intervention across all three axes.

Significance. If the claims are fully supported, the work would be practically significant: SL-Agent offers a way to exploit unlabeled interaction logs without human reward annotation; SGP-TOD provides a training-free alternative to fine-tuning for new task schemas; Self-Alignment points to a DPO-based route to factuality using the model's own knowledge signal. The experiments are broad and include comparisons with several strong baselines, ablations, and human evaluations. However, the strongest advertised property—zero-human-annotation adaptation to truly unseen behaviors—is weakened by the simulation design, and the factuality pipeline delegates key steps to an external model and uses golden-answer supervision during SK-Tuning. The contribution is therefore a useful set of empirical frameworks rather than a fully autonomous self-learning system.

major comments (3)

[§3.3.2, Eq. (3.2), Table 3.4] The simulation evidence for zero-annotation adaptability is circular as presented. The reward model is trained to discriminate five hand-defined corruption categories in §3.2.3 (repetition, inconsistency, partial information, non-fluency, misunderstanding), and the 'unseen' human-bot logs in §3.3.2 are generated 'by introducing noise through response corruption.' If the same corruption taxonomy was used to create the simulated logs, then Table 3.4 demonstrates that SL-Agent can recognize and correct the exact error types its reward model was built to detect, not that it generalizes to genuinely novel user behaviors. The manuscript should specify the corruption procedure in the simulation and, for a load-bearing test, hold out one or more error categories from reward training. The real-scenario experiment (Table 3.5) is less circular but uses only 30 logs and does not measure reward-model
[§5.2.1–5.2.3, Eq. (5.3)] The factuality framework is self-referential: preference labels are derived from the same model's self-evaluation p(True|q,a), and DPO then trains that model to prefer responses with high self-evaluation scores. Systematic overconfidence or task-specific bias in SELF-EVAL will therefore be reinforced rather than corrected. The manuscript acknowledges overconfidence in §5.2.2 and introduces SK-Tuning as mitigation, but SK-Tuning itself requires ground-truth answers and Deberta-Large-MNLI entailment to construct True/False labels, so the pipeline is not purely self-supervised. Moreover, claim extraction and question generation are delegated to GPT-3.5-turbo, so the 'self' is partly external. The paper should quantify how much of the DPO gain survives when preference labels are replaced by oracle factuality labels, and should compare against a non-self-referential reward model.
[§4.2.4, §4.3.1, Table 4.1] The zero-shot extensibility claim rests on manually engineered task schemas: belief instructions contain all slot names and plausible values, and policy skeletons contain 10–20 hand-written template turns per task. The 'zero-shot' label is standard in the prompting literature, but the central claim of 'minimal human effort' is sensitive to schema-engineering cost. The manuscript should include an explicit accounting of the human effort needed to author a new schema (or a study of how much performance degrades when the schema is imperfect), and it should state that the method does not remove the need for symbolic task design.

minor comments (4)

[§3.3.2, Table 3.4] The text says 'Table 4.2 presents the end-to-end evaluation results'; this should be Table 3.4. Similar cross-reference errors appear in §3.3.3 ('reported in Table 4.2') and §3.2.2.
[Table 3.4, Table 3.5] Significance is reported only as 'p < 0.01 based on Combined' without stating the test, the number of runs, or the variance. Given that the table reports per-domain results, a paired test across domains or a confidence interval would be more informative.
[§5.2.2, Table 5.1] The prompt shown uses a True/False format, but the narrative sometimes refers to 'A'/'B' as the output. Clarify whether the reported p(True) is the probability assigned to the 'True' token or the probability of the letter 'A'.
[Chapter 3–5] The manuscript repeatedly uses inconsistent spacing in model names such as 'S OLOIST', 'SL-S OLOIST', and 'S GP-TOD'. A final formatting pass is needed.

Circularity Check

1 steps flagged · score 4.0 of 10

One self-referential reward loop in the factuality chapter; adaptability and extensibility chapters are externally benchmarked and not demonstrated circular.

self definitional [Section 5.2.1 Overview (Steps 2-3), Section 5.2.2 Eq. 5.1, Section 5.2.3 Eq. 5.3]
"In this step, we evaluate the factuality of the generated candidate responses ... by leveraging the intrinsic knowledge of LLMs. ... we select the top α responses as the preferred responses y_w and the remaining responses as the dis-preferred ones y_l, resulting in a set of preference pairs D = {(x,y_w,y_l)}. ... Finally, we align the LLM with these preference data via DPO. (Sec. 5.2.1) ... p(True|q,a) = f_M(q,a) (Eq. 5.1)"

The preference labels used for DPO are produced by SELF-EVAL, which is built on the same LLM M whose responses it judges (p(True|q,a)=f_M(q,a)). DPO (Eq. 5.3) then fits the policy to prefer exactly those responses that M's own evaluator scores higher. If p(True) is miscalibrated for a claim, the loop reinforces the error rather than correcting it; the paper itself concedes overconfidence with SELF-EVAL-P(TRUE) and adds external SK-Tuning with golden answers to patch the signal. The external test benchmarks and SK-Tuning provide independent evidence, so the circularity is partial: the training labels are self-defined, but the final factuality claim is externally evaluated.

full rationale

The thesis contains three largely independent contributions. SGP-TOD (Ch. 4) is a prompting strategy evaluated against external benchmarks (MultiWOZ, RADDLE, STAR, domain-extension); the manual policy skeleton is derived from a few training dialogs, which weakens the 'zero-shot' label but is not a circular derivation. SL-Agent (Ch. 3) trains a reward model on human-annotated error categories and then applies it to unlabeled logs; this is a legitimate two-stage pipeline. The main unresolved risk is the simulation: Section 3.3.2 says the 45 'unseen' dialogs are made imperfect 'by introducing noise through response corruption,' while Section 3.2.3 defines the reward model's negative examples using five specific corruption categories. The manuscript does not explicitly state that the simulation corruptions are the same five categories, so I do not count this as a demonstrated reduction, but the lack of specification leaves the zero-annotation adaptability claim vulnerable to that critique. The clearest circular feature is in Chapter 5: the factuality preference data are labeled by the model's own self-evaluation (Eq. 5.1) and then used as DPO targets (Eq. 5.3), making the raw reward loop self-referential. The paper acknowledges overconfidence and adds SK-Tuning with external golden labels, and the final evaluation is external, so the central claim still has independent content. Overall score 4.

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

The thesis contributes three frameworks, each resting on domain assumptions about model self-evaluation and hand-crafted knowledge. No novel physical or conceptual entities are introduced.

free parameters (2)

top fraction alpha for preference selection
In Self-Alignment, the top alpha fraction of responses are marked preferred for DPO; value is chosen by hand, not derived, and affects preference data quality.
DPO beta
Regularization strength in DPO (Eq. 5.3) controls deviation from reference policy; chosen as hyperparameter, not fitted.

assumptions (4)

domain assumption A pre-trained reward model can assess response quality in unlabeled human-bot logs, including novel user behaviors.
SL-Agent's RL loop relies on the reward model to provide correct quality signals (Section 3.2.3).
domain assumption An LLM's self-evaluation probability p(True|q,a) correlates with factual correctness.
Self-Alignment uses self-evaluation as the preference label for DPO (Section 5.2.2).
domain assumption Hand-crafted task schemas (ontology plus dialog flow) are sufficient to guide a frozen LLM to complete new tasks.
SGP-TOD relies on the premise that a schema captures all needed task knowledge (Section 4.2).
standard math REINFORCE policy gradient and DPO loss are valid optimization objectives.
Used as training algorithms (Eq. 3.3, Eq. 5.3).

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Cite this review

Pith. "Pith review of Building Task Bots with Self-learning for Enhanced Adaptability, Extensibility, and Factuality." pith.science (2026). https://pith.science/paper/WL35XDB3

@misc{pith2026250819689,
  author       = {Pith},
  title        = {Pith review of: Building Task Bots with Self-learning for Enhanced Adaptability, Extensibility, and Factuality},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/WL35XDB3}},
  note         = {Machine review of arXiv:2508.19689}
}

read the original abstract

Developing adaptable, extensible, and accurate task bots with minimal or zero human intervention is a significant challenge in dialog research. This thesis examines the obstacles and potential solutions for creating such bots, focusing on innovative techniques that enable bots to learn and adapt autonomously in constantly changing environments.

Figures

Figures reproduced from arXiv: 2508.19689 by the authors.

**Figure 1.1.** Architectures of two end-to-end task-oriented dialogue systems: [PITH_FULL_IMAGE:figures/full_fig_p018_1_1.png] view at source ↗

**Figure 2.1.** An example of a dialog turn sequence at turn [PITH_FULL_IMAGE:figures/full_fig_p028_2_1.png] view at source ↗

**Figure 2.2.** Evolution of training paradigms for developing end-to-end task bots: [PITH_FULL_IMAGE:figures/full_fig_p030_2_2.png] view at source ↗

Figures from the paper (30 more)

**Figure 2.3.** Figure 2.3: An illustrative example of a dialog model that employs an auto-regressive [PITH_FULL_IMAGE:figures/full_fig_p032_2_3.png]

**Figure 2.4.** Figure 2.4: Illustration of proactive learning in a self-feeding chatbot. The model [PITH_FULL_IMAGE:figures/full_fig_p037_2_4.png]

**Figure 2.5.** Figure 2.5: Illustration of the machine teaching process [ [PITH_FULL_IMAGE:figures/full_fig_p038_2_5.png]

**Figure 2.6.** Figure 2.6: Dialog policy optimization in an RL loop, where the interaction between a [PITH_FULL_IMAGE:figures/full_fig_p039_2_6.png]

**Figure 2.7.** Figure 2.7: An example of a task schema from the MultiWOZ dataset [ [PITH_FULL_IMAGE:figures/full_fig_p043_2_7.png]

**Figure 2.8.** Figure 2.8: An overview of the ANYTOD system, cited from Zhao et al. [ [PITH_FULL_IMAGE:figures/full_fig_p045_2_8.png]

**Figure 2.9.** Figure 2.9: Illustration of the prompting paradigm in zero-shot, one-shot, and few-shot [PITH_FULL_IMAGE:figures/full_fig_p047_2_9.png]

**Figure 2.10.** Figure 2.10: An example of hallucinations in LLMs: given the same prompt, an LLM [PITH_FULL_IMAGE:figures/full_fig_p050_2_10.png]

**Figure 2.11.** Figure 2.11: A diagram illustrating the three steps of Reinforcement Learning from [PITH_FULL_IMAGE:figures/full_fig_p053_2_11.png]

**Figure 2.12.** Figure 2.12: Comparison of PPO vs. DPO, where DPO optimizes for human prefer [PITH_FULL_IMAGE:figures/full_fig_p055_2_12.png]

**Figure 3.1.** Figure 3.1: Illustration of the proposed SL-AGENT with a human-bot dialog example. (i) The human-bot dialog example, containing an inappropriate response related to unseen user behaviors (upper part). (ii) Demonstration of the refining process in SLAGENT with the exhibited dial…

**Figure 3.2.** Figure 3.2: The proposed SL-AGENT operates as follows: (i) Fine-tune the bot using available task-specific dialogs. (ii) Deploy the bot online to gather unlabeled humanbot dialog logs. (iii) Refine the dialog model using reinforcement learning with the fine-tuned reward model. …

**Figure 3.3.** Figure 3.3: Illustration of synthetic dialog construction. Slot values in the delexicalized [PITH_FULL_IMAGE:figures/full_fig_p065_3_3.png]

**Figure 3.4.** Figure 3.4: The summarized five types of dialog turns featuring inappropriate or in [PITH_FULL_IMAGE:figures/full_fig_p067_3_4.png]

**Figure 3.5.** Figure 3.5: Illustration of the training example, i.e., the processed dialog turn in the [PITH_FULL_IMAGE:figures/full_fig_p074_3_5.png]

**Figure 3.6.** Figure 3.6: Two interactive examples. (a) An interactive example between user and [PITH_FULL_IMAGE:figures/full_fig_p082_3_6.png]

**Figure 4.1.** Figure 4.1: The proposed SGP-TOD is depicted with a dialog example, where the [PITH_FULL_IMAGE:figures/full_fig_p087_4_1.png]

**Figure 4.2.** Figure 4.2: Illustration of belief state prediction utilizing DST Prompter. The predicted [PITH_FULL_IMAGE:figures/full_fig_p090_4_2.png]

**Figure 4.3.** Figure 4.3: Illustration of system action determination and response generation em [PITH_FULL_IMAGE:figures/full_fig_p092_4_3.png]

**Figure 4.4.** Figure 4.4: Zero-shot end-to-end evaluation results on [PITH_FULL_IMAGE:figures/full_fig_p103_4_4.png]

**Figure 4.5.** Figure 4.5: Detailed belief instructions in DST Prompter. [PITH_FULL_IMAGE:figures/full_fig_p112_4_5.png]

**Figure 4.6.** Figure 4.6: A formatting example in Policy Prompter. [PITH_FULL_IMAGE:figures/full_fig_p113_4_6.png]

**Figure 4.7.** Figure 4.7: Policy Prompter of SGP-TOD on STAR. The relevant template turn within the input, the generated user template utterance, and the system action in the output are accentuated. 99 [PITH_FULL_IMAGE:figures/full_fig_p114_4_7.png]

**Figure 4.8.** Figure 4.8: Policy Prompter of SGP-TOD-E2E on [PITH_FULL_IMAGE:figures/full_fig_p115_4_8.png]

**Figure 5.1.** Figure 5.1: Illustration of Self-Alignment for Factuality. Given a prompt to write a biography, before factuality alignment, the LLM generates some facts that are not accurate. Through self-evaluation, the LLM is capable of identifying these inaccurate facts. The feedback from t…

**Figure 5.2.** Figure 5.2: A diagram illustrating the three steps of our [PITH_FULL_IMAGE:figures/full_fig_p120_5_2.png]

**Figure 5.3.** Figure 5.3: The process of constructing training data for SK-T [PITH_FULL_IMAGE:figures/full_fig_p121_5_3.png]

**Figure 5.4.** Figure 5.4: Results of pairwise comparisons on BioGEN across four dimensions: factuality, helpfulness, relevance and naturalness, as evaluated by GPT-4. The left and right sections present the win rates of Self-Alignment for Factuality w/ SELF-EVAL-SKT against FACTTUNE-MC and S…

**Figure 5.5.** Figure 5.5: Calibration curves of utilizing SELF-EVAL-P(TRUE) and SELF-EVALSKT on LLAMA2-7B in the CommonsenseQA task. Following Kadavath et al. [65], we plot confidence vs. frequency that a prediction is correct. The dashed line indicates perfect calibration. tion. We present …

**Figure 5.6.** Figure 5.6: Calibration curves of utilizing SELF-EVAL-P(TRUE) and SELF-EVALSKT (without duplicates) on LLAMA2-7B in the CommonsenseQA task. Following Kadavath et al. [65], we plot confidence vs. frequency that a prediction is correct. The dashed line indicates perfect calibrati…

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Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.

[1] [1]

URL: " 'urlintro :=

ENTRY address author booktitle chapter edition editor howpublished institution journal key month note number organization pages publisher school series title type volume year eprint doi pubmed url lastchecked label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block STRINGS urlintro eprinturl eprintpr...

[2] [2]

write newline

" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...

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arXiv 2023

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arXiv 2023

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Anthropic. 2024. https://www.anthropic.com/news/claude-3-5-sonnet Claude 3.5 sonnet . Anthropic Blog

2024

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arXiv 2021

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doi:10.18653/v1/2023.findings-emnlp.68 2023

[8] [8]

Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, Anna Chen, Nova DasSarma, Dawn Drain, Stanislav Fort, Deep Ganguli, Tom Henighan, Nicholas Joseph, Saurav Kadavath, Jackson Kernion, Tom Conerly, Sheer El-Showk, Nelson Elhage, Zac Hatfield-Dodds, Danny Hernandez, Tristan Hume, Scott Johnston, Shauna Kravec, Liane Lovitt, Neel Nanda, Catherine Olsson, ...

arXiv 2022