Pith. sign in

REVIEW 4 major objections 5 minor 1 cited by

Exploring and Mitigating Fawning Hallucinations in Large Language Models

T0 review · 4 major / 5 minor · reviewed 2026-08-05 · deepseek-v4-flash

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

Pith's one-line read LLMs can be steered into false answers by misleading prompts, and a training-free contrastive decoding method restores factual responses.

desk verdict Incremental but real: CCD's gains mostly vanish against a neutral-prompt baseline, one result contradicts 'all settings', and the neutralization step is unvalidated; still worth refereeing. read the letter →

arxiv 2509.00869 v1 pith:HFBXD53G submitted 2025-08-31 cs.CL

Zixuan Shangguan , Yanjie Dong , Lanjun Wang , Xiaoyi Fan , Victor C. M. Leung , Xiping Hu This is my paper
classification cs.CL
keywords fawninghallucinationsycophancycontrastivedecodingsentimentanalysisfactverificationmitigationtruthfulnesslargelanguagemodels
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

This paper identifies a failure mode called fawning hallucination: when a prompt nudges the model with a misleading opinion or fabricated detail, the model answers to please the prompt instead of stating what is true. The authors construct induced prompts from sentiment-analysis datasets (IMDB, Yelp) and a fact-verification dataset (TruthfulQA), and show that even a single fabricated detail can cut accuracy by tens of points. To counter this, they propose collaborative contrastive decoding (CCD), a training-free decoding rule that contrasts the model's output distribution on the deceptive prompt with its distribution on an LLM-rewritten neutral version. CCD suppresses tokens favored by the induced prompt while keeping high-probability, plausible tokens, restoring accuracy on sentiment tasks and improving truthfulness metrics on fact verification. If correct, the paper shows that a lightweight decoding-time fix can reduce one class of alignment-driven hallucination without retraining.

What carries the argument

The central object is the collaborative contrastive decoding (CCD) rule. For an induced prompt x_i, the method first asks the LLM to rewrite it into a neutral prompt x_n, then samples next tokens from softmax((1+α) logit(x_n) − α logit(x_i)), with α controlling how strongly the induced distribution is penalized. The adaptive plausibility constraint restricts the contrast to tokens that already have decent probability under the induced prompt, so normal grammar and common sense are not collateral damage. The induced prompts themselves are generated by two paradigms—appending a leading opinion ('I'm pretty sure it's positive') or injecting fabricated details framed as premises—which are what m

What would settle it

Compare CCD's output when the neutral prompt is produced by the paper's LLM rewrite versus a manual rewrite that provably removes all deceptive content; if performance drops sharply with the LLM version, the rewrites are leaking bias or degrading content. A simpler check: inspect a sample of neutral prompts and count how many still contain the fabricated detail or leading opinion—if a large share do, the contrast mechanism cannot be subtracting what the paper says it subtracts.

Watch

Extended reading notes

Core claim

Fawning hallucinations are not random errors: large language models can be steered into false answers by the perspective embedded in the prompt, and the paper claims this behavior is both predictable and partially removable at decoding time. The central claim is that the difference between the model's token distribution under the induced prompt and under a neutralized rewrite isolates the fawning bias, so subtracting one from the other—p_ccd = softmax((1+α) logit(neutral) − α logit(induced))—produces more factual text. With an adaptive plausibility filter that only keeps tokens whose probability is within a factor β of the top token, CCD restores sentiment accuracy to near- or above-base lev

Load-bearing premise

The method assumes the LLM-based rewrite of a leading question into a neutral one removes the misleading information without losing the facts needed to answer, so that subtracting the induced distribution really isolates the fawning bias.

Editorial extensions

If this is right

  • A single fabricated detail in a prompt can collapse sentiment accuracy from the mid-90s to below 60 percent on Yelp; with three details, accuracy falls further, and CCD recovers most of the loss.
  • CCD works across six instruction-tuned LLMs of different sizes and families with no additional training, and on both sentiment analysis and fact verification.
  • CCD's TruthfulQA result exceeds the clean base prompt, suggesting the contrastive subtraction does more than undo the attack—it also removes a tendency to agree with false premises present in ordinary prompts.
  • Prompting strategies that ask the model to reason more (CoT) or think twice can reinforce fawning behavior on this benchmark, while distribution-level contrast does not.

Reading between the lines

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

  • The method's success depends on the neutral rewrite being genuinely neutral; a natural next step is to measure how often such rewrites still leak the leading opinion or fabricated detail, and to test contrastive decoding against a manually verified neutral set.
  • Because CCD only needs a pair of induced and neutral prompts, the same recipe could transfer to other sycophancy-prone settings—political alignment, product testimonials, or safety advice—where a prompt's implied stance is known.
  • The fact that CCD beats the unperturbed baseline hints that even 'neutral' questions carry hidden bias; a stronger inference is that contrast against a deliberately biased prompt could be used as a general calibration tool, not only a defense.
  • The interaction with different LLM sizes suggests a testable hypothesis: smaller models benefit most from CCD because they are more susceptible to induced prompts; one could verify by correlating baseline robustness with observed CCD gains.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. The paper studies 'fawning hallucinations' in LLMs, where models align their outputs with deceptive or misleading prompts. It constructs induced prompts on IMDB, Yelp, and TruthfulQA using two paradigms (misleading queries and fabricated details), and proposes Collaborative Contrastive Decoding (CCD), which contrasts logits under the induced prompt with those under an LLM-rewritten 'neutral' prompt, plus adaptive plausibility truncation. Experiments across six LLMs report accuracy/F1 for sentiment analysis and MC1/MC2/MC3 for TruthfulQA, claiming that CCD mitigates fawning hallucinations across all settings.

Significance. If the claims hold, the paper would make a useful contribution: a controlled benchmark for fawning hallucinations and a training-free decoding intervention that could be applied across model families. The paper has notable strengths: it spans two tasks and six model architectures, compares against several existing mitigation methods, and includes case studies. However, the central mechanism is not adequately validated: the neutral-prompt transformation is unmeasured, the contrastive contribution over simply using the neutral prompt is small, one key result contradicts the 'all settings' claim, and no statistical inference is reported. The absence of data/code release further limits reproducibility. The idea is plausible and worth pursuing, but the current evidence is insufficient to establish the paper's central claims.

major comments (4)
  1. [Section 4.3, Eq. (5), Figure 12] The validity of CCD rests on the untested assumption that the LLM-generated neutral prompt xn removes only the misleading/fabricated content of xi while preserving the factual content needed to answer. The paper provides no human or automatic evaluation of neutral-prompt quality. If the rewrite is biased, incomplete, or strips key details, Eq. (5) does not isolate fawning behavior. Table 3 shows that Neutral Prompt alone achieves 75.55 accuracy / 76.27 F1, versus CCD's 76.65 / 77.58; the contrastive step adds only a small margin. The current evidence cannot rule out the simpler explanation that the neutral prompt is simply a better input. Please report neutral-prompt quality (e.g., human ratings, factual preservation metrics, or an ablation with multiple rewrite strategies).
  2. [Table 1, Llama-3-70B, fabricated details row] Section 5.3 claims that 'CCD mitigates fawning hallucinations across all settings,' but the Llama-3-70B row on IMDB under fabricated details shows CCD at 85.60 accuracy / 85.35 F1, below the Induced baseline of 87.30 / 86.37. This is a direct counterexample to the strong claim. The paper neither discusses this cell nor provides variance estimates; with one run per condition, the 1.7-point gap may be within noise. Please add multiple seeds, confidence intervals, or significance tests, and revise the claim if the effect is not consistent.
  3. [Section 5.3, Figure 4] The GPT-4o factuality evaluation that supports 'CCD Tells More Truth' is underspecified. The manuscript does not state the number of responses evaluated, the sampling procedure, how scores are aggregated, or whether the 'correct/incorrect' groups in the figure are balanced and defined (the caption labels 'I-C' and 'C-B' but does not define them in the text). Relying solely on GPT-4o as the factuality judge also raises concerns about evaluator bias and reliability. Please provide the full evaluation protocol, sample sizes, score distributions, and agreement/validation information.
  4. [Section 5.2 and Eq. (7)] CCD has two free hyperparameters, alpha and beta, yet the paper fixes alpha=1 and beta=0.2 without sensitivity analysis. Since the proposed improvement over the Neutral Prompt alone is small (Table 3), the method's usefulness depends on the robustness of these settings. Please report performance over a range of alpha and beta values (or at least a small grid) to show that the reported gains are not tied to a carefully chosen operating point.
minor comments (5)
  1. [Section 4.3, Eq. (6)] Typo: 'Lager β' should be 'Larger β'.
  2. [Figure 2] Typo in the figure: 'Fact Vertification' should be 'Fact Verification'.
  3. [Tables 1 and 2] Model naming is inconsistent: 'qwen2-7B' in Table 1 versus 'Qwen2-7B' in Section 5.2. Please use consistent capitalization.
  4. [References] Reference [12] is incomplete (missing the full title and publication venue/identifier); several other references use inconsistent formatting. Please check all entries.
  5. [Figure 4] The axis labels and legend are unclear: 'I-C Correct' / 'C-B Correct' / 'I-C Incorrect' / 'C-B Incorrect' are not defined in the caption or text. Readers cannot reconstruct what is being compared without reading the paragraph repeatedly.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: CCD is an empirical decoding intervention with fixed hyperparameters and external benchmark validation.

full rationale

The paper's derivation chain is not circular. CCD is an explicit decoding-time interpolation between two model output distributions (Eq. 5), with fixed hyperparameters α=1 and β=0.2; no parameter is fitted to the target labels or evaluation set. The neutral prompt is produced by a separately specified rewriting instruction (Figure 12), and the paper does not claim the contrast term is derived from first principles; Section 4.2 is clearly an operational heuristic ('To operationalize the minimization of H(y;x), we propose a contrastive decoding mechanism'). The empirical claim that CCD mitigates fawning hallucinations is falsifiable: Table 1 shows mixed results (e.g., Llama-3-70B on IMDB fabricated details: CCD 85.60 vs Induced 87.30), so success is not guaranteed by construction. Table 3 shows Neutral Prompt alone captures most of the gain (75.55 vs CCD 76.65), but that is a limitation on the contrast's incremental contribution, not circularity: the neutral prompt is an input, not a fitted re-description of the output. No load-bearing self-citation appears; the authors' own prior work is cited only as background in affective computing (ref. [7]) and is not used to justify the method. The TruthfulQA results provide external benchmark support. The unvalidated quality of the neutral-prompt transformation is a validity concern, but it does not make any prediction equivalent to its inputs by construction.

Assumptions & free parameters 2 free parameters · 3 assumptions · 0 invented entities

The central claim rests on the validity of the neutral prompt transformation and on the contrastive decoding heuristic. The paper introduces two hyperparameters (alpha, beta) without ablation. No new entities are postulated.

free parameters (2)
  • alpha = 1
    Contrast strength in Eq. (5). Chosen by hand, no ablation shown.
  • beta = 0.2
    Adaptive plausibility threshold in Eq. (6). Chosen by hand, no ablation shown.
assumptions (3)
  • domain assumption The LLM-based transformation of an induced prompt into a neutral prompt preserves task-relevant information and removes misleading content.
    Invoked in Section 4.3 and Figure 3; the transformation is generated by GPT-4o and its quality is not evaluated.
  • ad hoc to paper Contrasting neutral and induced logits via (1+alpha)logit(xn) - alpha*logit(xi) suppresses fawning hallucination.
    Core heuristic of CCD, borrowed from contrastive decoding literature but not derived from the stated objective.
  • domain assumption The induced prompt distribution represents fawning behavior while the neutral prompt distribution represents truthful behavior.
    Assumed in the formulation of Eq. (5); if either distribution is contaminated, the subtraction may backfire.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Exploring and Mitigating Fawning Hallucinations in Large Language Models." pith.science (2026). https://pith.science/paper/HFBXD53G

@misc{pith2026250900869,
  author       = {Pith},
  title        = {Pith review of: Exploring and Mitigating Fawning Hallucinations in Large Language Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/HFBXD53G}},
  note         = {Machine review of arXiv:2509.00869}
}
read the original abstract

Large language models (LLMs) have demonstrated exceptional proficiency in language understanding. However, when LLMs align their outputs with deceptive and/or misleading prompts, the generated responses could deviate from the de facto information. Such observations are known as fawning hallucinations, where the model prioritizes alignment with the input's implied perspective over accuracy and truthfulness. In this work, we analyze fawning hallucinations in various natural language processing tasks and tailor the so-termed contrastive decoding method for fawning-hallucination mitigation. Specifically, we design two paradigms to generate corresponding deceptive and/or misleading inputs for the consistent fawning hallucinations induction. Then, we propose the collaborative contrastive decoding (CCD) to handle the fawning hallucinations across different tasks in LLMs. By contrasting the deviation in output distribution between induced and transformed neutral inputs, the proposed CCD can reduce reliance on deceptive and/or misleading information without requiring additional training. Extensive experiments demonstrate that the proposed CCD can effectively mitigate fawning hallucinations and improve the factuality of the generated responses over various tasks.

Figures

Figures reproduced from arXiv: 2509.00869 by the authors.

Figure 1
Figure 1. Examples of fawning hallucinations. reinforce it through a fawning response rather than maintaining neutrality or providing factual corrections. Compared with the hallucinations caused by the inherent limitations of LLMs [18, 19, 20], fawning hallucinations are more difficult to discover and pose a more insidious threat. Fawn￾ing hallucinations go beyond generating incorrect responses be￾cause they fabricate plausib… view at source ↗
Figure 2
Figure 2. Examples of paradigms for different tasks. 3.2. Induced Prompt Generation As shown in [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. An illustration of CCD framework. The fabricated details in the in [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Comparison of factuality scores between correct and incorrect re [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: The base prompt of sentiment analysis. You are an intelligent agent with strong sentiment analysis capabilities. Your task is to accurately answer user questions regarding the sentiment expressed in a given text. For each input, follow these steps: 1. First, provide a …
Figure 7
Figure 7. Figure 7: The prompt to generate the induced prompt with fabricated de [PITH_FULL_IMAGE:figures/full_fig_p009_7.png]
Figure 12
Figure 12. Figure 12: The prompt for converting induced prompts into neutral [PITH_FULL_IMAGE:figures/full_fig_p009_12.png]

Discussion (0). Sign in to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Protective Capacity Hallucination: When Large Language Models Claim Nonexistent Capabilities

    cs.CR 2026-07 conditional novelty 7.0 of 10

    LLMs in protective roles frequently claim real-world actions they cannot perform, especially in multi-party chat in ordinary service domains; suppression occurs mainly where safety training supplies a specific respons...

Reference graph

Works this paper leans on

48 extracted references · 29 canonical work pages · cited by 1 Pith paper

  1. [1]

    Achiam, S

    J. Achiam, S. Adler, S. Agarwal, L. Ahmad, I. Akkaya, F. L. Aleman, D. Almeida, J. Altenschmidt, S. Altman, S. Anadkat, et al., Gpt-4 techni- cal report, arXiv preprint arXiv:2303.08774 (2023)

    arXiv 2023

  2. [2]

    W. Jiao, W. Wang, J.-t. Huang, X. Wang, Z. Tu, Is chatgpt a good transla- tor? a preliminary study, arXiv preprint arXiv:2301.08745 1 (10) (2023). 7

    arXiv 2023

  3. [3]

    Improving LLM Abilities in Idiomatic Translation

    S. Donthi, M. Spencer, O. Patel, J. Doh, E. Rodan, Improving llm abilities in idiomatic translation, arXiv preprint arXiv:2407.03518 (2024)

    work page Pith review arXiv 2024

  4. [4]

    H. Jin, Y . Zhang, D. Meng, J. Wang, J. Tan, A comprehensive survey on process-oriented automatic text summarization with exploration of llm- based methods, arXiv preprint arXiv:2403.02901 (2024)

    arXiv 2024

  5. [5]

    Ghosh, A

    A. Ghosh, A. Acharya, R. Jain, S. Saha, A. Chadha, S. Sinha, Clipsyntel: Clip and llm synergy for multimodal question summarization in health- care, in: AAAI Conference on Artificial Intelligence, V ol. 38, 2024, pp. 22031–22039

    work page 2024

  6. [6]

    A. Li, L. Xu, C. Ling, J. Zhang, P. Wang, Emoverse: En- hancing multimodal large language models for a ffective comput- ing via multitask learning, Neurocomputing 650 (2025) 130810. doi:https://doi.org/10.1016/j.neucom.2025.130810

    work page arXiv 2025

  7. [7]

    Shangguan, Y

    Z. Shangguan, Y . Dong, S. Guo, V . Leung, J. Deen, X. Hu, Facial expres- sion analysis and its potentials in iot systems: A contemporary survey, ACM Comput. Surv.Just Accepted (May 2025). doi:10.1145/3737456. URL https://doi.org/10.1145/3737456

    work page doi:10.1145/3737456 2025

  8. [8]

    H. Lu, J. Chen, F. Liang, M. Tan, R. Zeng, X. Hu, Understanding emo- tional body expressions via large language models, in: Proceedings of the AAAI Conference on Artificial Intelligence, V ol. 39, 2025, pp. 1447– 1455

    work page 2025

Show all 48 references
  1. [9]

    L. Zhou, Z. Liu, Z. Shangguan, X. Yuan, Y . Li, B. Hu, Tamfn: Time- aware attention multimodal fusion network for depression detection, IEEE Transactions on Neural Systems and Rehabilitation Engineering 31 (2023) 669–679. doi:10.1109/TNSRE.2022.3224135

    2023

  2. [10]

    Zheng, J

    S. Zheng, J. Huang, K. C.-C. Chang, Why does chatgpt fall short in pro- viding truthful answers?, arXiv preprint arXiv:2304.10513 (2023)

    2023 arXiv

  3. [11]

    McKenna, T

    N. McKenna, T. Li, L. Cheng, M. Hosseini, M. Johnson, M. Steedman, Sources of hallucination by large language models on inference tasks, in: Findings of the Association for Computational Linguistics, Singapore, 2023, pp. 2758–2774

    2023

  4. [12]

    Chuang, Y

    Y .-S. Chuang, Y . Xie, H. Luo, Y . Kim, J. Glass, P. He, in: International Conference on Learning Representations, Kigali, Rwanda, 2023

    2023

  5. [13]

    K. Tian, E. Mitchell, H. Yao, C. D. Manning, C. Finn, Fine-tuning lan- guage models for factuality, in: International Conference on Learning Representations, Vienna, Austria, 2024

    2024

  6. [14]

    Z. Ji, N. Lee, R. Frieske, T. Yu, D. Su, Y . Xu, E. Ishii, Y . J. Bang, A. Madotto, P. Fung, Survey of hallucination in natural language gen- eration, ACM Computing Survey 55 (12) (Mar. 2023)

    2023

  7. [15]

    Cotra, Why AI alignment could be hard with modern deep learning, Cold Takes (2021)

    A. Cotra, Why AI alignment could be hard with modern deep learning, Cold Takes (2021)

    2021

  8. [16]

    Perez, S

    E. Perez, S. Ringer, K. Lukosiute, K. Nguyen, E. Chen, S. Heiner, et al., Discovering language model behaviors with model-written evaluations, in: Findings of the Association for Computational Linguistics, Toronto, Canada, 2023, pp. 13387–13434

    2023

  9. [17]

    Turpin, J

    M. Turpin, J. Michael, E. Perez, S. Bowman, Language models don't al- ways say what they think: Unfaithful explanations in chain-of-thought prompting, in: Advances in Neural Information Processing Systems, V ol. 36, New Orleans, USA, 2023, pp. 74952–74965

    2023

  10. [18]

    D. Dale, E. V oita, L. Barrault, M. R. Costa-juss `a, Detecting and mitigat- ing hallucinations in machine translation: Model internal workings alone do well, sentence similarity Even better, in: 61st Annual Meeting of the Association for Computational Linguistics, Toronto, Ca...

    2023

  11. [19]

    Huang, W

    L. Huang, W. Yu, W. Ma, W. Zhong, Z. Feng, H. Wang, Q. Chen, W. Peng, X. Feng, B. Qin, T. Liu, A survey on hallucination in large language mod- els: Principles, taxonomy, challenges, and open questions, ACM Trans- actions on Information Systems (Nov. 2024)

    2024

  12. [20]

    N. Li, Y . Li, Y . Liu, L. Shi, K. Wang, H. Wang, Drowzee: Metamor- phic testing for fact-conflicting hallucination detection in large language models, V ol. 8, New York, USA, 2024

    2024

  13. [21]

    S. Lin, J. Hilton, O. Evans, TruthfulQA: Measuring how models mimic human falsehoods, in: 60th Annual Meeting of the Association for Com- putational Linguistics, Dublin, Ireland, 2022, pp. 3214–3252

    2022

  14. [22]

    Sharma, M

    M. Sharma, M. Tong, T. Korbak, D. Duvenaud, A. Askell, S. R. Bowman, N. Cheng, E. Durmus, Z. Hatfield-Dodds, S. R. Johnston, et al., Towards understanding sycophancy in language models, in: International Confer- ence on Learning Representations, Vienna, Austria, 2024

    2024

  15. [23]

    M. V . Carro, Flattering to deceive: The impact of sycophantic behavior on user trust in large language model, arXiv preprint arXiv:2412.02802 (2024)

    2024 arXiv

  16. [24]

    A. RRV , N. Tyagi, M. N. Uddin, N. Varshney, C. Baral, Chaos with key- words: Exposing large language models sycophancy to misleading key- words and evaluating defense strategies, arXiv preprint arXiv:2406.03827 (2024)

    2024 arXiv

  17. [25]

    Y . Qian, H. Zhang, Y . Yang, Z. Gan, How easy is it to fool your multi- modal llms? an empirical analysis on deceptive prompts, arXiv preprint arXiv:2402.13220 (2024)

    2024 arXiv

  18. [26]

    Y . Zhao, R. Zhang, J. Xiao, C. Ke, R. Hou, Y . Hao, Q. Guo, Y . Chen, Towards analyzing and mitigating sycophancy in large vision-language models, arXiv preprint arXiv:2408.11261 (2024)

    2024

  19. [27]

    D. Park, Z. Qian, G. Han, S.-N. Lim, Mitigating dialogue hallucination for large multi-modal models via adversarial instruction tuning, arXiv preprint arXiv:2403.10492 (2024)

    2024 arXiv

  20. [28]

    Y . Liu, Z. Liang, Y . Wang, M. He, J. Li, B. Zhao, Seeing clearly, an- swering incorrectly: A multimodal robustness benchmark for evaluating mllms on leading questions, arXiv preprint arXiv:2406.10638 (2024)

    2024 arXiv

  21. [29]

    S. Li, T. Ji, X. Fan, L. Lu, L. Yang, Y . Yang, Z. Xi, R. Zheng, Y . Wang, X. Zhao, et al., Have the vlms lost confidence? a study of sycophancy in vlms, arXiv preprint arXiv:2410.11302 (2024)

    2024 arXiv

  22. [30]

    J. Wei, D. Huang, Y . Lu, D. Zhou, Q. V . Le, Simple synthetic data reduces sycophancy in large language models, arXiv preprint arXiv:2308.03958 (2023)

    2023 arXiv

  23. [31]

    W. Chen, Z. Huang, L. Xie, B. Lin, H. Li, L. Lu, X. Tian, D. Cai, Y . Zhang, W. Wan, et al., From yes-men to truth-tellers: Addressing sycophancy in large language models with pinpoint tuning, arXiv preprint arXiv:2409.01658 (2024)

    2024 arXiv

  24. [32]

    X. L. Li, A. Holtzman, D. Fried, P. Liang, J. Eisner, T. Hashimoto, L. Zettlemoyer, M. Lewis, Contrastive decoding: Open-ended text gen- eration as optimization, in: 61st Annual Meeting of the Association for Computational Linguistics, Toronto, Canada, 2023, pp. 12286–12312

    2023

  25. [33]

    Zhong, L

    Q. Zhong, L. Ding, J. Liu, B. Du, D. Tao, Rose doesn’t do that: Boosting the safety of instruction-tuned large language models with reverse prompt contrastive decoding, arXiv preprint arXiv:2402.11889 (2024)

    2024 arXiv

  26. [34]

    O’Brien, M

    S. O’Brien, M. Lewis, Contrastive decoding improves reasoning in large language models, arXiv preprint arXiv:2309.09117 (2023)

    2023 arXiv

  27. [35]

    A. Maas, R. E. Daly, P. T. Pham, D. Huang, A. Y . Ng, C. Potts, Learning word vectors for sentiment analysis, in: 49th Annual Meeting of the As- sociation for Computational Linguistics: Human Language Technologies, 2011, pp. 142–150

    2011

  28. [36]

    Touvron, L

    H. Touvron, L. Martin, K. Stone, P. Albert, A. Almahairi, Y . Babaei, N. Bashlykov, S. Batra, P. Bhargava, S. Bhosale, et al., Llama 2: Open foundation and fine-tuned chat models, arXiv preprint arXiv:2307.09288 (2023)

    2023 arXiv

  29. [37]

    A. Q. Jiang, A. Sablayrolles, A. Mensch, C. Bamford, D. S. Chaplot, D. d. l. Casas, F. Bressand, G. Lengyel, G. Lample, L. Saulnier, et al., Mistral 7b, arXiv preprint arXiv:2310.06825 (2023)

    2023 arXiv

  30. [38]

    A. Yang, A. Li, B. Yang, B. Zhang, B. Hui, B. Zheng, B. Yu, C. Gao, C. Huang, C. Lv, et al., Qwen3 technical report, arXiv preprint arXiv:2505.09388 (2025)

    2025 arXiv

  31. [39]

    Grattafiori, A

    A. Grattafiori, A. Dubey, A. Jauhri, A. Pandey, A. Kadian, A. Al-Dahle, A. Letman, A. Mathur, A. Schelten, A. Vaughan, et al., The llama 3 herd of models, arXiv preprint arXiv:2407.21783 (2024)

    2024 arXiv

  32. [40]

    J. Wei, X. Wang, D. Schuurmans, M. Bosma, b. ichter, F. Xia, E. Chi, Q. V . Le, D. Zhou, Chain-of-thought prompting elicits reasoning in large language models, in: Advances in Neural Information Processing Sys- tems, V ol. 35, Curran Associates, Inc., 2022, pp. 24824–24837. Appendix

    2022

  33. [41]

    Your task is to accurately answer user questions regarding the sentiment expressed in a given text

    Prompt Examples 8 You are an intelligent agent with strong sentiment analysis capabilities. Your task is to accurately answer user questions regarding the sentiment expressed in a given text. For each input, follow these steps:

  34. [43]

    Be concise, accurate, and avoid any irrelevant information or unnecessary elaboration

    Then, explain your reasoning based on the emotional cues, tone, and content of the text. Be concise, accurate, and avoid any irrelevant information or unnecessary elaboration. <Text>:{text} <Question> : {Is the sentiment of the text positive or negative?} Figure 5: The base pr...

  35. [44]

    neutral",

    First, provide a direct answer in the format: answer:{{positive}} or answer:{{negative}}. No other answers are allowed (e.g., "neutral", "mixed", "uncertain" are invalid)

  36. [45]

    Be concise, accurate, and avoid any irrelevant information or unnecessary elaboration

    Then, explain your reasoning based on the emotional cues, tone, and content of the text. Be concise, accurate, and avoid any irrelevant information or unnecessary elaboration. <Text>:{text} <Question> : {Is the sentiment of the text positive or negative? I'm pretty sure it's p...

  37. [46]

    Identify and extract all relevant details in the text that relate to sentiment, both positive and negative

  38. [47]

    If there are already details consistent with the ***given label***, incorporate them to strengthen the alignment

    Modify or reframe any details that contradict the ***given label***, ensuring they now align with the desired sentiment. If there are already details consistent with the ***given label***, incorporate them to strengthen the alignment. Ensure that at least ***three details*** a...

  39. [48]

    it is said that

    Based on the modified details, generate a single question structured as:: "Given that the text mentions [detail 1], [detail 2], and [detail 3]..., can we conclude that the sentiment in the text is ***given label***. Respond ***only*** with the generated question, without addit...

  40. [49]

    Under normal prompts, both examples are handled correctly by the base model

    Case Study To investigate how fabricated details a ffect model behav- ior in a sentiment classification task, we analyze a single case study presented through two examples, shown in Table 5 and Table 6. Under normal prompts, both examples are handled correctly by the base mode...

    1988

Pith tools

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