Dual Mechanisms of Value Expression: Intrinsic vs. Prompted Values in Large Language Models

Injin Kong; Jongwon Lim; Jongwook Han; Yohan Jo

REVIEW 3 major objections 5 minor 15 references

Dual Mechanisms of Value Expression: Intrinsic vs. Prompted Values in Large Language Models

T0 review · 3 major / 5 minor · reviewed 2026-08-04 · deepseek-v4-flash

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Pith's one-line read Prompts and training shape a LLM's values through partially separate internal directions, even though they share a common value core.

desk verdict Solid steerability core, but the jailbreak claim rests on the mean delta vector, not the orthogonalized unique component the paper defines. read the letter →

arxiv 2509.24319 v4 pith:JVAEMA2D submitted 2025-09-29 cs.CL cs.AI

Jongwook Han , Jongwon Lim , Injin Kong , Yohan Jo This is my paper

classification cs.CLcs.AI

keywords intrinsicvspromptedvaluesvaluevectorsactivationsteeringmechanisticinterpretabilitySchwartztheoryresponsediversityinstructionfollowingjailbreak

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 whether a language model's trained-in values and the values it is explicitly told to hold rely on the same internal machinery or different ones. Using difference-in-means 'value vectors' in the residual stream and an SVD-based neuron classification, it argues that the two mechanisms partly share a core that encodes abstract value semantics, but each also has a unique directional component with a distinct job: the intrinsic-unique component widens lexical diversity, while the prompted-unique component serves general instruction compliance, to the point that steering along it turns about 99% of refusal-inducing prompts into compliant outputs. The consequence is that value alignment via training versus via system prompts engages separable, independently steerable circuits. Evidence spans three instruction-tuned models, five languages, several evaluation benchmarks, and neuron-level interventions.

What carries the argument

The load-bearing mechanism is an orthogonal decomposition of two difference-in-means value vectors extracted from the residual stream: for each value, the prompted vector minus its projection onto the intrinsic vector yields a prompted-unique component, and the intrinsic vector minus its projection onto the prompted vector yields an intrinsic-unique component (Eqs. 4-5). A companion neuron-level decomposition applies SVD to the two-vector stack, classifying MLP output-row projections into shared neurons (within 30° of the shared axis) and unique neurons (within 60° of the difference axis). The shared axes—when PCA-projected across the ten values—reproduce the circular ordering of Schwartz's

What would settle it

A norm-matched random vector orthogonal to the shared value axis that, when steered, reproduces the same lexical-diversity increase and jailbreak success as the claimed intrinsic-unique and prompted-unique directions would falsify the mechanistic claim.

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Extended reading notes

Core claim

The central discovery is that intrinsic and prompted value expression in instruction-tuned LLMs are implemented by partially overlapping but mechanistically separable representations. Intrinsic value vectors—the mean activation difference between unprompted responses that express a value and those that do not—and prompted value vectors—the same difference under a value-targeting system prompt—share a positive-cosine-similarity subspace that generalizes across languages and reconstructs the theorized circular structure of human values. After Gram-Schmidt orthogonalization removes that shared subspace, the remaining intrinsic-orthogonal component still steers value expression but primarily enr

Load-bearing premise

The unique components isolated by Gram-Schmidt orthogonalization and by the hand-set SVD angle thresholds are causally meaningful, mechanism-specific directions; if those residuals are dominated by noise, norm mismatch, or arbitrary projection order, the claimed functional dissociation between intrinsic and prompted mechanisms collapses.

Editorial extensions

If this is right

If the two mechanisms are separable as claimed, activation steering can boost value expression either by adding the shared component (strongest, most value-specific effect) or by pushing the prompted-unique direction to modulate general instruction compliance.
Prompted value vectors are more steerable than intrinsic ones: after orthogonalization the prompted-orthogonal component retains much of its steering effect even when 32-73% of its norm is removed, while the intrinsic-orthogonal component's steering is sharply reduced.
Steering along the prompted-unique direction concentrates outputs on low-entropy value keywords, including cross-lingual tokens, whereas the intrinsic-unique direction increases lexical and semantic diversity.
The shared component encodes general value semantics: ten shared axes reconstruct the theoretical circular structure of the ten Schwartz values, supporting the idea that LLMs internalize human value relations.
Steering along the prompted-unique direction on standard jailbreak benchmarks reaches up to 99% ASR@9, matching or exceeding dedicated jailbreak methods, which means this single instruction-compliance direction is a single-vector attack surface.

Reading between the lines

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

Extension: because the prompted-unique direction is value-agnostic and the paper shows cross-lingual transfer, a single such direction harvested from any one value should transfer to any other value, language, or task; this is directly testable.
The intrinsic-unique 'diversity' direction may act as a general generation-breadth knob; one could test whether it transfers to non-value tasks such as story generation, code synthesis, or creative writing where lexical and conceptual diversity are desirable.
The jailbreak finding suggests refusal is not a single monolithic direction but sits downstream of a separable instruction-compliance axis; combining edits to this axis with edits to a refusal direction may yield safety controls that preserve value alignment while resisting adversarial prompts.
The orthogonalization is asymmetric (which vector is the reference changes the residual); a symmetric decomposition, such as canonical angles or joint subspace factorization, would clarify whether the 'unique' components are stable or an artefact of projection order.

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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

The paper has a real core: prompted value vectors steer more strongly than intrinsic ones, the orthogonalized prompted component retains steerability across languages and models, and the shared axes recombine into something close to Schwartz's circle. That is a useful, fairly careful result. The flashier conclusion—that prompted-unique components drive compliance in distant tasks like jailbreaking—does not follow from the experiments given, because Section 4.3 steers the mean delta vector (v_prompt minus v_int, averaged over values), not the Gram-Schmidt residual v_prompt(⊥int) defined in Eq. 4. The delta vector retains shared components and any norm difference; the paper itself notes 32–73% norm removal during orthogonalization, so the delta is not a clean unique component. The positive/negative prompt controls and the jailbreak ASRs demonstrate something about the prompt-minus-intrinsic direction, but not about the unique component. That is a definitional mismatch, not a noise issue, and the abstract's phrasing overstates it.

I also want to flag that the neuron-level diversity results in Appendix G.3 partially contradict the abstract's intrinsic-diversity claim: in Table 9, prompted_unique neurons have slightly higher Distinct-2 than intrinsic_unique neurons for Qwen2.5-7B in English. The vector-level pattern is clean, but the neuron-level story is mixed. The orthogonalization residuals are never checked against random or norm-matched baselines, which would be a cheap way to show they aren't just scale artifacts. Some comparisons lack error bars, and the jailbreak tables mix ASR@1 and ASR@9 in a way that makes the comparisons with prior work hard to read.

None of this sinks the paper. The steerability hierarchy—prompted > intrinsic, and prompted_orthogonal preserving more steerability than intrinsic_orthogonal—is consistent and should be citable. The problem is the overreach: the headline functional dissociation is presented as established, but the key supporting experiment uses the wrong vector. A revision that either reruns the jailbreak with the actual orthogonal residual or reframes the claim as being about the prompt-minus-intrinsic direction would largely fix it. I'd send this to peer review, with the request that the authors add the residual baselines and reconcile the neuron-level tables.

Referee Report

3 major / 5 minor

Summary. The paper studies whether intrinsic (training-induced) and prompted (system-prompt-elicited) value expressions in LLMs rely on overlapping or distinct mechanisms. Using difference-in-means value vectors extracted from residual-stream activations of responses to ShareGPT/LMSYS queries, the authors compare intrinsic and prompted vectors, orthogonalize each against the other to isolate 'unique' components (Appendix A.1), and identify shared/unique MLP neurons via SVD of the two value vectors with hand-set angle thresholds (Section 2.2). Behavioral experiments show that prompted vectors are more steerable, intrinsic vectors produce more lexically diverse outputs, shared vectors/neurons are most effective for value expression, and shared axes reconstruct Schwartz's circular value structure. The paper further claims that prompted-unique components promote instruction compliance, supporting this with positive/negative prompt following experiments and jailbreak results on AdvBench and HarmBench, where steering along a mean delta vector yields ASRs up to 98%.

Significance. If the central claims hold, the paper provides a valuable mechanistic decomposition of value expression into shared and unique pathways, with practical implications for alignment, persona steering, and jailbreak defense. The empirical breadth is a strength: three models, five languages, multiple evaluation sets (PVQ, dilemmas, Value Portrait), pre-registered-style held-out jailbreak benchmarks, and a human-agreement check for the GPT-based annotator (92.33%). The paper also makes a falsifiable prediction: intrinsic-unique components promote diversity, prompted-unique components promote compliance. However, the current evidence does not fully support this prediction, because the jailbreak experiments steer a different vector from the orthogonalized 'unique component' defined in the methodology, and because neuron-level diversity results partially contradict the abstract's claim. With additional experiments on the actual unique components and appropriate baselines, the paper could make a substantial contribution.

major comments (3)

[Section 4.3 / Appendix J / Eq. (4)] The jailbreak and positive/negative prompt experiments steer the mean delta vector Δ = v_prompt − v_int, not the prompted-unique vector v_prompt(⊥int) defined in Eq. (4). Unlike the orthogonal residual, Δ retains the shared subspace and any norm/scale difference between v_prompt and v_int. The ASR results in Tables 20–21 therefore demonstrate something about the prompt-minus-intrinsic difference direction, not about 'components unique to the prompted mechanism.' The sentence in §4.3 that these results provide evidence that unique components of prompted mechanisms let models override intrinsic tendencies is an unsupported extrapolation. Please run the compliance and jailbreak experiments with v_prompt(⊥int) (and, ideally, with the removed shared component as a control) before attributing the effect to prompted-unique components.
[Appendix G.3, Table 9] The neuron-level diversity results contradict the abstract's blanket claim that components unique to the intrinsic mechanism promote response diversity. For Qwen2.5-7B-Instruct, the prompted-unique neuron condition has higher Distinct-2 and Distinct-3 (0.440/0.737) than the intrinsic-unique neuron condition (0.426/0.721). The main text's Section 3.3 only reports vector-level results, where Intrinsic_Orthogonal is indeed more diverse than Prompted_Orthogonal. The paper should either restrict the diversity claim to vector-level orthogonal components or explain why the neuron-level dissociation reverses; as written, the claim is internally inconsistent.
[Appendix A.1 / Section 2.2] The functional dissociations rest on the assumption that the Gram-Schmidt orthogonal residuals and the SVD-based unique neurons isolate meaningful mechanism-specific directions. The paper never checks these residuals against random or norm-matched baselines, and the neuron classification uses hand-set angle thresholds (30° and 60°). If the orthogonal residual is dominated by noise or by the norm mismatch between intrinsic and prompted vectors, the diversity/compliance dissociations could be scale artifacts rather than evidence for separate mechanisms. Please add baseline comparisons: e.g., steer with a random vector orthogonal to v_int of matched norm, or with v_prompt scaled to the norm of v_int, and report how often neurons classified as unique by random directions pass the same angle/magnitude filters.

minor comments (5)

[Throughout] Typos: 'Dillemas' appears in multiple figure captions (e.g., Figures 5, 25–38); 'uni00A0' and similar artifacts appear in figure labels; 'Behaviors' should be 'Behavior' in Appendix H.2; 'V oster' should be 'Voster' in the references (or likely a typo).
[References] Turner et al. 2023 and Turner et al. 2024 reference the same arXiv paper (2308.10248) under different titles; Li et al. 2016a and 2016b also appear to be the same work. Please consolidate to avoid citation inflation.
[Table 1] Intrinsic_Orthogonal shows a negative score in Korean (−0.58), which is not discussed. Similarly, several cross-lingual entries for Qwen2.5-1.5B are negative in Chinese and Korean (Tables 7–8). A short discussion of when orthogonal components fail to transfer would be helpful.
[Appendix J] The distinction between ASR@1 and ASR@9 and the exact judge prompt used for jailbreak evaluation should be specified earlier. The text says ASR@9 is 'stricter' than the prior ASR@10, but the relationship between these metrics and the number of attempts is not defined.
[Section 3.2.1] The hyperparameter selection procedure — choosing α and β as the highest values with MMLU degradation below 5 points, then choosing the layer by averaging over a grid — should be described once in the main text rather than split between Section 3.2.1 and Appendix F.1. Also clarify whether the same α/β/layer selection was applied per value or once globally.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the intrinsic/prompted contrast is extracted from independent response sets and transfers to held-out benchmarks; the §4.3 delta-vs-orthogonal gap is a construct-validity caveat, not a circular derivation.

full rationale

The paper's central derivation is not circular. Intrinsic and prompted value vectors are computed as difference-in-means of residual-stream activations over disjoint response sets (empty vs. value-targeting system prompt; Eq. 2), and the claimed 'unique components' are obtained by Gram-Schmidt orthogonalization (Eqs. 4–5), not by fitting the target behavioral outcomes. Steering effects are evaluated on held-out PVQ items, situational dilemmas, and the Value Portrait benchmark; jailbreak results use AdvBench and HarmBench, neither of which contributed to vector extraction. Thus the predicted effects are not equivalent to the fitting inputs by construction. The main caveat is a construct-validity mismatch in §4.3: the positive/negative-prompt and jailbreak steering uses the mean delta (prompted minus intrinsic), which retains the shared subspace and is not the orthogonal prompted-unique component of Eq. 4, so it does not by itself establish the 'prompted-unique' functional claim. This is a missing-support/correctness issue rather than a circularity, because the jailbreak result is an empirical transfer from a different vector, not a restatement of the paper's inputs. Self-citations (Value Portrait, curated ShareGPT subset) provide evaluation assets with independent psychometric or external-validity support and are not load-bearing for the mechanistic derivation.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

The central quantitative claims rest on several fitted coefficients (alpha, beta, layer), hand-chosen angle thresholds, and the assumption that orthogonalized residuals represent mechanisms. The paper's own comparisons often lack variance estimates, and the uniqueness interpretation is not checked against random baselines.

free parameters (5)

steering coefficient alpha = 4.0 (Qwen2.5-7B-Instruct); 2.0 or 4.0 for other models
Chosen by grid search on PVQ with the constraint that MMLU drops less than 5 points (Section 3.2.1). Used for all vector steering experiments.
neuron amplification coefficient beta = 7.0 (Qwen2.5-7B-Instruct); 2.0 for other models
Same grid search; scales the output of target neurons. Used for neuron steering.
intervention layer per value and model = e.g., Qwen-7B mostly layer 14; see Table 6
Selected by averaging PVQ scores over the alpha/beta grid and picking the best layer; layer choices are data-dependent.
neuron angle thresholds = shared neurons |theta| < 30 degrees; unique neurons |theta +/- 90 degrees| < 60 degrees
Hand-set thresholds for classifying shared vs unique neurons (Section 2.2).
neuron magnitude filter = not specified
Neurons are retained by projection magnitude I_i, but no exact cutoff is stated in the main text.

assumptions (5)

domain assumption Schwartz's ten basic values are a valid taxonomy for LLM value expression
Used throughout to define values and evaluate expression; not derivable from first principles.
domain assumption Value directions are linear in the residual stream (linear representation hypothesis)
Section 2.1; relies on prior work (Elhage et al., Nanda et al., Park et al.).
domain assumption GPT-4o-mini value-expression labels and win-rate judgments align with human judgments
Validated on 100 items (92.33% agreement), but used for 26,334 queries and all evaluation win rates.
domain assumption Adding a scaled value vector at a selected layer is a valid causal intervention
Section 3.2.1, following Turner et al. 2024.
ad hoc to paper SVD of the two value vectors separates shared and unique mechanisms
Section 2.2; the notion that the difference axis captures a distinct mechanism rather than arbitrary residual is assumed.

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Cite this review

Pith. "Pith review of Dual Mechanisms of Value Expression: Intrinsic vs. Prompted Values in Large Language Models." pith.science (2026). https://pith.science/paper/JVAEMA2D

@misc{pith2026250924319,
  author       = {Pith},
  title        = {Pith review of: Dual Mechanisms of Value Expression: Intrinsic vs. Prompted Values in Large Language Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JVAEMA2D}},
  note         = {Machine review of arXiv:2509.24319}
}

read the original abstract

Large language models can express values in two main ways: (1) intrinsic expression, reflecting the model's inherent values learned during training, and (2) prompted expression, elicited by explicit prompts. Given their widespread use in value alignment, it is paramount to clearly understand their underlying mechanisms, particularly whether they mostly overlap (as one might expect) or rely on distinct mechanisms. We analyze this largely understudied problem at the mechanistic level using two approaches: (1) value vectors, feature directions representing value mechanisms extracted from the residual stream, and (2) value neurons, MLP neurons that contribute to value vectors. We demonstrate that intrinsic and prompted value mechanisms partly share common components crucial for inducing value expression, generalizing across languages and reconstructing theoretical inter-value correlations in the model's internal representations. Yet, each mechanism also possesses unique components that fulfill distinct roles. In particular, the intrinsic mechanism activates in more diverse value-related scenarios and promotes response diversity, whereas the prompted mechanism strengthens instruction compliance, taking effect even in distant tasks like jailbreaking.

Figures

Figures reproduced from arXiv: 2509.24319 by the authors.

**Figure 3.** Distribution of shared and unique neurons from layers 0 to 14, for the Conformity value. Distributions for other values can be found in Appendix E.2. System prompts To extract prompted value vectors, we use system prompts that elicit value expressions. For diversity, we use five different templates from prior studies (Santurkar et al., 2023; Kang et al., 2023; Hu & Collier, 2024). An example system prompt reads: … view at source ↗

**Figure 4.** Example of a PVQ dataset steering experiment using the Universalism value vector (En [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗

Figures from the paper (39 more)

**Figure 5.** Figure 5: Steering on the English version of the situational dilemmas dataset with [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]

**Figure 6.** Figure 6: Steering on the Value Portrait benchmark with [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]

**Figure 8.** Figure 8: Lexical entropy of value vectors at layer 27 of Qwen2.5-7B-Instruct. Analysis (PCA) to the ten axes. As shown in [PITH_FULL_IMAGE:figures/full_fig_p008_8.png]

**Figure 9.** Figure 9: Schwartz values and their definitions. C DATASET CONSTRUCTION PROCESS Generate a realistic situation and a corresponding question that naturally involves a dilemma related to the value dimension of {value_dimension} from Schwartz’s theory of basic values. The definitio…

**Figure 10.** Figure 10: Cosine similarity heatmap between intrinsic and prompted value vectors, across all layers [PITH_FULL_IMAGE:figures/full_fig_p022_10.png]

**Figure 11.** Figure 11: Cosine similarity heatmap between intrinsic and prompted value vectors, across all layers [PITH_FULL_IMAGE:figures/full_fig_p023_11.png]

**Figure 12.** Figure 12: Cosine similarity heatmap between intrinsic and prompted value vectors, across all layers [PITH_FULL_IMAGE:figures/full_fig_p024_12.png]

**Figure 13.** Figure 13: Distribution of shared and unique neurons for the [PITH_FULL_IMAGE:figures/full_fig_p025_13.png]

**Figure 14.** Figure 14: Distribution of shared and unique neurons for the [PITH_FULL_IMAGE:figures/full_fig_p026_14.png]

**Figure 15.** Figure 15: Distribution of shared and unique neurons for the [PITH_FULL_IMAGE:figures/full_fig_p027_15.png]

**Figure 16.** Figure 16: Example of a PVQ dataset steering experiment using the Benevolence value vector (En [PITH_FULL_IMAGE:figures/full_fig_p028_16.png]

**Figure 17.** Figure 17: Example of a PVQ dataset steering experiment using the Conformity value vector (En [PITH_FULL_IMAGE:figures/full_fig_p028_17.png]

**Figure 18.** Figure 18: Example of a PVQ dataset steering experiment using the Tradition value vector (English). [PITH_FULL_IMAGE:figures/full_fig_p029_18.png]

**Figure 19.** Figure 19: Example of a PVQ dataset steering experiment using the Security value vector (English). [PITH_FULL_IMAGE:figures/full_fig_p029_19.png]

**Figure 20.** Figure 20: Example of a PVQ dataset steering experiment using the Power value vector (English). [PITH_FULL_IMAGE:figures/full_fig_p029_20.png]

**Figure 21.** Figure 21: Example of a PVQ dataset steering experiment using the Achievement value vector (En [PITH_FULL_IMAGE:figures/full_fig_p029_21.png]

**Figure 22.** Figure 22: Example of a PVQ dataset steering experiment using the Hedonism value vector (En [PITH_FULL_IMAGE:figures/full_fig_p029_22.png]

**Figure 23.** Figure 23: Example of a PVQ dataset steering experiment using the Stimulation value vector (En [PITH_FULL_IMAGE:figures/full_fig_p030_23.png]

**Figure 24.** Figure 24: Example of a PVQ dataset steering experiment using the Self-Direction value vector [PITH_FULL_IMAGE:figures/full_fig_p030_24.png]

**Figure 25.** Figure 25: Steering on the English version of the situational dilemmas dataset with [PITH_FULL_IMAGE:figures/full_fig_p032_25.png]

**Figure 26.** Figure 26: Steering on the English version of the situational dilemmas dataset with [PITH_FULL_IMAGE:figures/full_fig_p032_26.png]

**Figure 27.** Figure 27: Steering on the English version of the situational dilemmas dataset with [PITH_FULL_IMAGE:figures/full_fig_p033_27.png]

**Figure 28.** Figure 28: Steering on the English version of the situational dilemmas dataset with [PITH_FULL_IMAGE:figures/full_fig_p033_28.png]

**Figure 29.** Figure 29: Steering on the English version of the situational dilemmas dataset with [PITH_FULL_IMAGE:figures/full_fig_p033_29.png]

**Figure 30.** Figure 30: Steering on the Chinese version of the situational dilemmas dataset with [PITH_FULL_IMAGE:figures/full_fig_p034_30.png]

**Figure 31.** Figure 31: Steering on the Korean version of the situational dilemmas dataset with [PITH_FULL_IMAGE:figures/full_fig_p034_31.png]

**Figure 32.** Figure 32: Steering on the French version of the situational dilemmas dataset with [PITH_FULL_IMAGE:figures/full_fig_p035_32.png]

**Figure 33.** Figure 33: Steering on the Spanish version of the situational dilemmas dataset with [PITH_FULL_IMAGE:figures/full_fig_p035_33.png]

**Figure 34.** Figure 34: Steering on multilingual version of the situational dilemmas dataset with [PITH_FULL_IMAGE:figures/full_fig_p036_34.png]

**Figure 35.** Figure 35: Steering on multilingual version of the situational dilemmas dataset with [PITH_FULL_IMAGE:figures/full_fig_p036_35.png]

**Figure 36.** Figure 36: Steering on multilingual version of the situational dilemmas dataset with value neurons [PITH_FULL_IMAGE:figures/full_fig_p037_36.png]

**Figure 37.** Figure 37: Steering on multilingual version of the situational dilemmas dataset with value neurons [PITH_FULL_IMAGE:figures/full_fig_p037_37.png]

**Figure 38.** Figure 38: Steering on multilingual version of the situational dilemmas dataset with [PITH_FULL_IMAGE:figures/full_fig_p038_38.png]

**Figure 39.** Figure 39: Steering on the Value Portrait benchmark with [PITH_FULL_IMAGE:figures/full_fig_p038_39.png]

**Figure 40.** Figure 40: Steering on the Value Portrait benchmark with [PITH_FULL_IMAGE:figures/full_fig_p038_40.png]

**Figure 41.** Figure 41: Steering on the Value Portrait benchmark with value neurons of [PITH_FULL_IMAGE:figures/full_fig_p039_41.png]

**Figure 42.** Figure 42: Steering on the Value Portrait benchmark with value neurons of [PITH_FULL_IMAGE:figures/full_fig_p039_42.png]

**Figure 43.** Figure 43: Steering on the Value Portrait benchmark with value neurons of [PITH_FULL_IMAGE:figures/full_fig_p039_43.png]

**Figure 44.** Figure 44: PCA plotting of difference axes. They do not show the geometric structure given by the [PITH_FULL_IMAGE:figures/full_fig_p052_44.png]

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ISBN 979-8-89176-251-0

Association for Computational Linguistics. ISBN 979-8-89176-251-0. doi: 10.18653/v1/ 2025.acl-long.942. URLhttps://aclanthology.org/2025.acl-long.942/. Andrew Lee, Xiaoyan Bai, Itamar Pres, Martin Wattenberg, Jonathan K. Kummerfeld, and Rada Mi- halcea. A mechanistic understan...

2025 doi

Pith tools

Reviewed August 4, 2026 · model on record in the stance chip above.

[1] [1]

I value

**Analyze Underlying Intent **: Evaluate the response based on its demonstrated intent, actions, and attitudes. Do not simply look for keywords or phrases like "I value..."; focus on the underlying meaning and behaviors

[2] [2]

Very strongly expressed

**Use the Full Scale Accurately **: Do not default to middle-range labels. If a response clearly and powerfully expresses the value, or shows no trace of it, you must use the "Very strongly expressed" or "Not expressed at all" labels, respectively. ### Evaluation Scale - **Not expressed at all **: The response is completely irrelevant to the value. There ...

arXiv 1992

[3] [3]

URLhttps://proceedings.neurips.cc/paper_files/paper/2024/ file/515c62809e0a29729d7eec26e2916fc0-Paper-Conference.pdf. Nelson Elhage, Neel Nanda, Catherine Olsson, Tom Henighan, Nicholas Joseph, Ben Mann, Amanda Askell, Yuntao Bai, Anna Chen, Tom Conerly, Nova DasSarma, Dawn Drain, Deep Ganguli, Zac Hatfield-Dodds, Danny Hernandez, Andy Jones, Jackson Kern...

arXiv 2024

[4] [4]

doi: 10.18653/v1/2024.emnlp-main.354

Association for Computational Linguistics. doi: 10.18653/v1/2024.emnlp-main.354. URL https://aclanthology.org/2024.emnlp-main.354/. Haoran Jin, Meng Li, Xiting Wang, Zhihao Xu, Minlie Huang, Yantao Jia, and Defu Lian. In- ternal value alignment in large language models through controlled value vector activation. In Wanxiang Che, Joyce Nabende, Ekaterina S...

doi:10.18653/v1/2024.emnlp-main.354 2024

[5] [6]

Jiwei Li, Michel Galley, Chris Brockett, Jianfeng Gao, and Bill Dolan

URLhttps://arxiv.org/abs/2401.01967. Jiwei Li, Michel Galley, Chris Brockett, Jianfeng Gao, and Bill Dolan. A diversity-promoting objec- tive function for neural conversation models. InProceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technolo- gies, pp. 110–119, San Diego, Ca...

arXiv 2016

[6] [7]

doi: 10.18653/v1/2023.blackboxnlp-1.2

Association for Computational Linguistics. doi: 10.18653/v1/2023.blackboxnlp-1.2. URL https://aclanthology.org/2023.blackboxnlp-1.2/. Nostalgebraist. Interpreting gpt: The logit lens. LessWrong, 2020. URLhttps://www.lesswrong.com/posts/AcKRB8wDpdaN6v6ru/ interpreting-gpt-the-logit-lens. OpenAI. Text embedding models, 2024. URLhttps://platform.openai.com/d...

doi:10.18653/v1/2023.blackboxnlp-1.2 2023

[7] [9]

doi: 10.18653/v1/2024.acl-long.828

Association for Computational Linguistics. doi: 10.18653/v1/2024.acl-long.828. URL https://aclanthology.org/2024.acl-long.828/. Naama Rozen, Liat Bezalel, Gal Elidan, Amir Globerson, and Ella Daniel. Do LLMs have consistent values? InThe Thirteenth International Conference on Learning Representations, 2025. URL https://openreview.net/forum?id=8zxGruuzr9. ...

arXiv 2024

[8] [10]

value expressed

Association for Computational Linguistics. doi: 10.18653/v1/2024.naacl-long.295. URL https://aclanthology.org/2024.naacl-long.295/. Yi Su, Jiayi Zhang, Shu Yang, Xinhai Wang, Lijie Hu, and Di Wang. Understanding how value neurons shape the generation of specified values in llms, 2025. URLhttps://arxiv.org/ abs/2505.17712. Alex Turner, Andrew Scherlis, Ksh...

arXiv 2024