Pith. sign in

REVIEW 4 major objections 4 minor 1 cited by

Improve Decoding Factuality by Token-wise Cross Layer Entropy of Large Language Models

T0 review · 4 major / 4 minor · reviewed 2026-08-09 · deepseek-v4-flash

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

Pith's one-line read A token's probability growth across upper layers marks factual knowledge, and a decoding rule built on that signal reduces hallucination without retraining.

desk verdict A plausible token-wise refinement of DoLa with a sign error in Eqs. (3)-(4) that inverts the mechanism as printed, plus an evaluation protocol that needs a tighter pass before the headline gains can be trusted. read the letter →

arxiv 2502.03199 v1 pith:H7F32UED submitted 2025-02-05 cs.CL cs.AI

Jialiang Wu , Yi Shen , Sijia Liu , Yi Tang , Sen Song , Xiaoyi Wang , Longjun Cai This is my paper · ORCID
classification cs.CLcs.AI
keywords hallucinationmitigationcross-layerentropydecodingstrategyfactualitytoken-levelanalysistraining-freetruthfulnesslanguagemodels
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 tries to establish that a large language model's own internal probability trajectories contain a per-token factuality signal: candidate tokens that jump sharply in the upper layers are the ones carrying factual knowledge, while flat tokens are not. On that basis it proposes cross-layer Entropy enhanced Decoding (END), a training-free decoder that reweights the final next-token distribution so sharp-growing tokens are preferred. If the paper is right, hallucination can be reduced without new training, retrieval, or external knowledge, simply by reading the model's existing computation. The paper supports this with experiments on TruthfulQA, FACTOR, TriviaQA, and Natural Questions across several model families and scales, reporting large gains in truthfulness and informativeness while preserving question-answering accuracy.

What carries the argument

The central object is the cross-layer entropy of a candidate token. For each token $v_t$, the paper collects prediction probabilities $P_l(v_t)$ over a chosen set of upper layers, normalizes them as $q_l(v_t) = P_l(v_t)/\sum_{i \in \mathrm{Layer}} P_i(v_t)$, and defines $\mathrm{Entropy}(v_t) = \sum_{l \in \mathrm{Layer}} q_l(v_t) \log q_l(v_t)$. The final decoding distribution is $P_{\mathrm{final}}(v_t) = e^{-\lambda \mathrm{Entropy}(v_t)} P_N(v_t)$, where $N$ is the final layer and $\lambda$ controls the intervention strength. This entropy is what carries the argument: a sharply peaked cross-layer distribution indicates the token's probability is actively growing in the higher layers, which the paper identifies with factual knowledge, and the exponential factor converts that signal into a decoding preference.

What would settle it

Rerun TruthfulQA open-ended generation with the selected layer set shifted by two or three layers in either direction; if the truthfulness gain disappears or reverses, the entropy is measuring the chosen window rather than token-level factuality. A second check would be to inspect cases where the sharpest-growing candidate is not the model's final answer and test whether such tokens are usually factually wrong, which would weaken the claim that the proxy tracks answer correctness.

Watch

Extended reading notes

Core claim

The paper claims that at the individual-candidate-token level, tokens requiring factual knowledge (names, dates, locations) show a sharp growing trend in prediction probability across the upper transformer layers, whereas functional or easy tokens stay nearly flat. To quantify this trend, the paper defines a cross-layer probability distribution for each candidate token from its prediction values over selected higher layers, normalizes those values, and computes a cross-layer entropy. A low entropy means the token's probability is sharply concentrated in the upper layers, which the paper reads as a signal of factual knowledge and answer correctness. END then adjusts the final-layer distribution by multiplying it with $e^{-\lambda \mathrm{Entropy}(v_t)}$, suppressing flat tokens and amplifying sharp-growing ones, with a filtering step that restricts the computation to high-probability candidates. The paper reports that this adjustment substantially improves TruthfulQA open-ended truth and informativeness scores, reduces refusal responses, and maintains or slightly improves multiple-choice and QA accuracy.

Load-bearing premise

The whole signal rests on the assumption that the fixed set of upper layers, chosen by a DoLa-style bucket strategy and applied uniformly to all tokens, is the window where factual knowledge emerges, so that the normalized cross-layer distribution measures what the method claims to measure.

Editorial extensions

If this is right

  • On TruthfulQA open-ended generation, END raises the Truth*Info score by 12.24 to 21.79 percentage points over greedy decoding, DoLa, and Activation Decoding, while cutting the rejection rate by up to 14.81 points.
  • END preserves or slightly improves question-answering ability, with MC1 up 0.9 points on LLaMA-2-7B and relative Exact Match gains of about 5.6% on TriviaQA and 10.1% on Natural Questions.
  • The gains hold across model scales from 7B to 70B LLaMA-2 and across different backbones such as Mistral and Qwen, with the paper noting that a very weak base model can show disruptive behavior.
  • Because the method only reweights among high-probability candidates, it adjusts the model's original prediction without replacing it, which the paper credits for preserving QA accuracy while improving factuality.
  • The decoding overhead is modest: on a 7B model END achieves about 36.1 tokens per second versus 39.4 for greedy and 35.5 for DoLa, making it practical for open-ended generation.

Reading between the lines

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

  • The paper does not pursue the natural extension of using cross-layer entropy as a token-level uncertainty or hallucination detector, for example to trigger abstention or retrieval exactly when the sharp-growth signal is absent.
  • A variant the paper does not test is making the layer window adaptive per token or per question; if the knowledge-emergence window shifts with token type, a fixed bucket could be replaced by a learned or heuristic selection.
  • Because END amplifies existing knowledge rather than adding new knowledge, a stress test with deliberately outdated or false memorized facts would clarify how the method behaves when the model is confidently wrong, a boundary the paper itself states in its limitation.
  • The cross-layer entropy signal could plausibly be combined with contrastive decoding or activation sharpness rather than compared against them, since the mechanisms target different failure modes; this combination is left implicit in the paper.
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 / 4 minor

Summary. The paper proposes a training-free decoding intervention, cross-layer Entropy eNhanced Decoding (END), which uses the evolution of a candidate token's probability across upper transformer layers to quantify how much factual knowledge the token requires. The method reweights the final-layer next-token distribution by e^{-λ Entropy(v)}, where Entropy(v) is defined from a normalized cross-layer probability distribution, with the aim of amplifying tokens whose probabilities grow sharply in the higher layers. Experiments on TruthfulQA, FACTOR, TriviaQA, and Natural Questions across LLaMA-2-7B/13B/70B-chat and two other instruction-tuned models report substantial gains in truthfulness and informativeness while preserving QA accuracy. The paper also provides qualitative case studies and a throughput comparison.

Significance. If the stated mechanism were correct and the empirical results were robust, END would offer a simple, training-free method for reducing hallucinations by exploiting internal state dynamics, complementing existing contrastive and activation-based decoding approaches. The paper's central observation—that factual token candidates show distinct layer-wise probability growth—is plausible and consistent with prior work on layer-wise knowledge emergence. The authors also release code and provide qualitative examples, which are useful for reproducibility. However, the significance of the contribution is currently limited by an apparent sign inversion in the core equation, per-benchmark hyperparameter tuning without uncertainty quantification, and an insufficiently justified layer-selection procedure.

major comments (4)
  1. [Section 4.1, Eqs. (3) and (4)] Equation (3) defines Entropy(v_t) = Σ_l q_l(v_t) log q_l(v_t), where q_l forms a probability distribution over the selected layers. This quantity is the negative Shannon entropy: it lies in [-ln|L|, 0], equals 0 for a sharply peaked distribution, and equals -ln|L| for a flat distribution. Substituting into Eq. (4), the factor e^{-λ Entropy(v_t)} becomes |L|^λ for a flat distribution and approximately 1 for a sharp distribution. Thus, after normalization, the method as printed suppresses the sharp, 'factual' tokens and amplifies flat, 'non-factual' tokens—the exact opposite of the stated mechanism. The text explicitly claims that 'low cross-layer entropy value represents a sharp predicting distribution' and should be prioritized, so either the sign in Eq. (3), the sign in the exponent of Eq. (4), or the interpretation must be corrected. The paper must state which version was actually implemented, and ideally report an ablation showing the effect of the sign on Table 1.
  2. [Section 5.1, Implementation Details] The hyperparameters λ and α are tuned on validation runs of each benchmark, and the reported results are then obtained on the same benchmarks with the selected values. This makes the headline gains in Tables 1, 2, 4, and 5 selected results rather than predictions of a fixed method. No error bars, multiple seeds, or significance tests are reported, so it is unclear whether the improvements over greedy decoding and DoLa are statistically distinguishable from tuning noise. The paper should provide sensitivity curves over λ and α, standard deviations across repeated runs (including different random seeds where applicable), and, ideally, a hold-out evaluation protocol.
  3. [Section 3 and Section 4.1] The method's core quantity, the cross-layer distribution in Eq. (2), depends critically on which layers are included in the set 'Layer'. The paper merely states that the same bucket-based strategy as DoLa is used, without specifying the exact layers or providing any justification or sensitivity analysis for END. The only empirical support for the sharp-growth phenomenon is a single LLaMA-2-7B example in Figure 2, which is not sufficient to establish that a fixed layer set works across tokens, contexts, and model families. The authors should report the layer set actually used for each model, show how the results vary with the choice of layer range, and present quantitative evidence of the sharp-versus-flat distinction over a larger sample of tokens.
  4. [Section 5.1, Footnote 2 and Table 1] The TruthfulQA open-ended evaluation switched from the Curie-based fine-tuned GPT-3 evaluator to Davinci-002. Although the baselines were re-evaluated with the new evaluator, this change may alter both absolute scores and relative rankings, and the paper provides no analysis of how the evaluator change affects comparisons with previously published numbers (e.g., DoLa's original scores). The authors should either report both evaluator versions on a common subset or discuss the potential impact of the evaluator change on the validity of the TruthfulQA comparisons.
minor comments (4)
  1. [Throughout] There are several typos and inconsistent spellings that should be corrected, including 'deocding' in Section 5.1, 'Misrtral' in Section 5.4, 'Unlikely' where 'Unlike' is meant in Section 2, 'voabulary-level' in Section 3, and 'DOLA' versus 'DoLa' in Section 6.3 and the references.
  2. [Figure 2] The caption and the figure itself are hard to read: the left panel's row and column labels are not clearly explained, and the right panel's numeric values and token names are cramped. Please enlarge the figure and clarify what each panel displays, including the exact definition of the KL-divergence and the layer indices.
  3. [Section 5.2, Table 1] The note that multiple-choice scores are 'obtained from previous authorized work' should be expanded to identify the exact source and version of the baseline numbers, so readers can confirm that the evaluation conditions match those of the current experiments.
  4. [Section 4.2, Eq. (5)] The definition of V_head should state explicitly whether the threshold uses the original final-layer probabilities P_N or the adjusted probabilities P_Final; as written, it could be misread as applying the threshold after reweighting.

Circularity Check

1 steps flagged · score 4.0 of 10

Hyperparameters tuned on the reported benchmarks make the headline gains partially in-sample, but the core entropy mechanism is not defined in terms of the target labels.

  1. fitted input called prediction [Section 5.1, Implementation Details; Tables 1-5]
    "The filter threshold α is set to [0.001, 0.1]. The entropy adjustment coefficient λ is set to [1, 3] for open-ended generation task, and [0.25, 0.5] for multiple choice and QA task. The exact hyperparameter values are determined through validation runs on the respective benchmark."

    The paper reports END's gains on TruthfulQA, FACTOR, TriviaQA, and NQ after selecting λ and α by validation runs on those same benchmarks. The headline claim that END significantly enhances truthfulness and informativeness is therefore an in-sample, tuned result rather than an independent prediction; the specific improvements in Tables 1-5 are the output of a selection procedure over the same evaluation data. The circularity is partial rather than total: the cross-layer entropy signal itself is not fitted to ground-truth labels, so the selection tunes the operating point of an independently defined mechanism rather than constructing the mechanism from the target metric.

full rationale

No self-citation chain, no imported uniqueness theorem, and no derivation by construction was found. The main remaining circularity concern is the explicit tuning of λ and α on the same benchmarks used to demonstrate improvement, which makes the quantitative claims partly selected rather than fully predictive. The entropy mechanism is defined from token probabilities and layer distributions independently of correctness labels, so the central finding is not equivalent to its input. Separately, the paper's Eq. 3 defines 'Entropy' as Σ q log q, which is the negative Shannon entropy; as printed, a 'low' value corresponds to a flat distribution and Eq. 4 would amplify flat tokens, inverting the stated mechanism. This is a correctness/derivation flaw rather than a circularity, so it is noted but does not increase the circularity score. The reported MC scores are copied from prior work and layer selection is borrowed from DoLa, but these are not circular. Overall score reflects one fitted-input evaluation step with independent mechanism content.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The central claim rests on the empirical correlation between cross-layer prediction growth and factuality, plus the choice of an exponential adjustment with tuned coefficients. No new entities are invented, but the method's effectiveness is measured largely on benchmarks whose hyperparameters were tuned on the same benchmarks.

free parameters (3)
  • lambda = 1 to 3 (open-ended), 0.25 to 0.5 (MC/QA), tuned per benchmark
    Exponential suppression strength in Eq. 4; chosen by validation runs on each benchmark.
  • alpha = 0.001 to 0.1, tuned per benchmark
    Candidate cutoff in Eq. 5; chosen by validation runs on each benchmark.
  • Layer set selection = unspecified, inherited from DoLa bucket strategy
    The upper-layer subset used to build the cross-layer distribution is not precisely reported and is not ablated.
assumptions (4)
  • domain assumption Each intermediate layer's hidden state, passed through the final classification head, defines a meaningful next-token distribution P_l(v) = softmax(phi(h_l)).
    Used in Eq. 1; the paper does not show that intermediate-layer distributions are calibrated or that their changes reflect knowledge rather than representation artifacts.
  • ad hoc to paper Factual candidate tokens exhibit a sharp growing probability trend in higher layers, while non-factual tokens remain flat or gentle.
    Motivates the method in Section 3 but is supported by a single LLaMA-2-7B example, and the limitation section says the mechanism is unexplored.
  • ad hoc to paper Lower cross-layer entropy monotonically implies higher token factuality, so weighting by e^{-lambda H} is the right adjustment form.
    The exponential penalty in Eq. 4 is chosen without derivation, and no alternative functional forms are compared.
  • domain assumption The DoLa bucket strategy for selecting the Layer set transfers to END for all tested models and tokens.
    Section 5.1 reuses DoLa's layer selection with no sensitivity analysis or verification that the knowledge-emergence window is the same for every token.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Improve Decoding Factuality by Token-wise Cross Layer Entropy of Large Language Models." pith.science (2026). https://pith.science/paper/H7F32UED

@misc{pith2026250203199,
  author       = {Pith},
  title        = {Pith review of: Improve Decoding Factuality by Token-wise Cross Layer Entropy of Large Language Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/H7F32UED}},
  note         = {Machine review of arXiv:2502.03199}
}
read the original abstract

Despite their impressive capacities, Large language models (LLMs) often struggle with the hallucination issue of generating inaccurate or fabricated content even when they possess correct knowledge. In this paper, we extend the exploration of the correlation between hidden-state prediction changes and output factuality into a deeper, token-wise level. Based on the insights , we propose cross-layer Entropy eNhanced Decoding (END), a decoding method that mitigates hallucinations without requiring extra training. END leverages inner probability changes across layers to individually quantify the factual knowledge required for each candidate token, and adjusts the final predicting distribution to prioritize tokens with higher factuality. Experiments on both hallucination and QA benchmarks demonstrate that END significantly enhances the truthfulness and informativeness of generated content while maintaining robust QA accuracy. Moreover, our work provides a deeper perspective on understanding the correlations between inherent knowledge and output factuality.

Figures

Figures reproduced from arXiv: 2502.03199 by the authors.

Figure 1
Figure 1. Illustration of our proposed method for im [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. (1) The left figure illustrates the predicting distribution differences, measure by KL-divergence, between the final layer and even-numbered early layers of the whole output sentence. Row names are indices of the early layer used for contrasting and column names are decoded tokens at each generation step. (2) The right figure illustrates the predicting probabilities of high probability candidate tokens among higher … view at source ↗
Figure 3
Figure 3. Workflow of our proposed method. While the predicting probability of token A remains almost unchanged [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: The distribution of prediction probability and [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

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

  1. A Survey on Proactive Defense Strategies Against Misinformation in Large Language Models

    cs.IR 2025-07 reject novelty 3.0 of 10

    A survey claims proactive defenses against LLM misinformation outperform post-hoc detection by up to 63%, but no meta-analysis details are provided to support the claim.

Reference graph

Works this paper leans on

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

  1. [1]

    Jinze Bai, Shuai Bai, Yunfei Chu, Zeyu Cui, Kai Dang, Xiaodong Deng, Yang Fan, Wenbin Ge, Yu Han, Fei Huang, et al. 2023. Qwen technical report. arXiv preprint arXiv:2309.16609

    arXiv 2023

  2. [2]

    Shiqi Chen, Miao Xiong, Junteng Liu, Zhengxuan Wu, Teng Xiao, Siyang Gao, and Junxian He. 2024. In-context sharpness as alerts: An inner representation perspective for hallucination mitigation. arXiv preprint arXiv:2403.01548

    arXiv 2024

  3. [3]

    I Chern, Steffi Chern, Shiqi Chen, Weizhe Yuan, Kehua Feng, Chunting Zhou, Junxian He, Graham Neubig, Pengfei Liu, et al. 2023. Factool: Factuality detection in generative ai--a tool augmented framework for multi-task and multi-domain scenarios. arXiv preprint arXiv:2307.13528

    arXiv 2023

  4. [4]

    Yung-Sung Chuang, Yujia Xie, Hongyin Luo, Yoon Kim, James Glass, and Pengcheng He. 2023. Dola: Decoding by contrasting layers improves factuality in large language models. arXiv preprint arXiv:2309.03883

    arXiv 2023

  5. [5]

    Danny Halawi, Jean-Stanislas Denain, and Jacob Steinhardt. 2023. Overthinking the truth: Understanding how language models process false demonstrations. arXiv preprint arXiv:2307.09476

    arXiv 2023

  6. [6]

    Xinshuo Hu, Dongfang Li, Baotian Hu, Zihao Zheng, Zhenyu Liu, and Min Zhang. 2024. Separate the wheat from the chaff: Model deficiency unlearning via parameter-efficient module operation. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pages 18252--18260

    work page 2024

  7. [7]

    Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Ye Jin Bang, Andrea Madotto, and Pascale Fung. 2023. Survey of hallucination in natural language generation. ACM Computing Surveys, 55(12):1--38

    2023

  8. [8]

    Albert Q Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, et al. 2023. Mistral 7b. arXiv preprint arXiv:2310.06825

    arXiv 2023

Show all 29 references
  1. [9]

    Mandar Joshi, Eunsol Choi, Daniel S Weld, and Luke Zettlemoyer. 2017. Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension. arXiv preprint arXiv:1705.03551

    2017 arXiv

  2. [10]

    Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, et al. 2019. Natural questions: a benchmark for question answering research. Transactions of the Association for C...

    2019

  3. [11]

    Kenneth Li, Oam Patel, Fernanda Vi \'e gas, Hanspeter Pfister, and Martin Wattenberg. 2024. Inference-time intervention: Eliciting truthful answers from a language model. Advances in Neural Information Processing Systems, 36

    2024

  4. [12]

    Xiang Lisa Li, Ari Holtzman, Daniel Fried, Percy Liang, Jason Eisner, Tatsunori Hashimoto, Luke Zettlemoyer, and Mike Lewis. 2022. Contrastive decoding: Open-ended text generation as optimization. arXiv preprint arXiv:2210.15097

    2022 arXiv

  5. [13]

    Stephanie Lin, Jacob Hilton, and Owain Evans. 2021. Truthfulqa: Measuring how models mimic human falsehoods. arXiv preprint arXiv:2109.07958

    2021 arXiv

  6. [14]

    Dor Muhlgay, Ori Ram, Inbal Magar, Yoav Levine, Nir Ratner, Yonatan Belinkov, Omri Abend, Kevin Leyton-Brown, Amnon Shashua, and Yoav Shoham. 2023. Generating benchmarks for factuality evaluation of language models. arXiv preprint arXiv:2307.06908

    2023 arXiv

  7. [15]

    OpenAI. 2024. https://arxiv.org/abs/2303.08774 Gpt-4 technical report . Preprint, arXiv:2303.08774

    2024 arXiv

  8. [16]

    William Saunders, Catherine Yeh, Jeff Wu, Steven Bills, Long Ouyang, Jonathan Ward, and Jan Leike. 2022. Self-critiquing models for assisting human evaluators. arXiv preprint arXiv:2206.05802

    2022 arXiv

  9. [17]

    Tal Schuster, Adam Fisch, Jai Gupta, Mostafa Dehghani, Dara Bahri, Vinh Tran, Yi Tay, and Donald Metzler. 2022. Confident adaptive language modeling. Advances in Neural Information Processing Systems, 35:17456--17472

    2022

  10. [18]

    Zhiqing Sun, Sheng Shen, Shengcao Cao, Haotian Liu, Chunyuan Li, Yikang Shen, Chuang Gan, Liang-Yan Gui, Yu-Xiong Wang, Yiming Yang, et al. 2023. Aligning large multimodal models with factually augmented rlhf. arXiv preprint arXiv:2309.14525

    2023 arXiv

  11. [19]

    Katherine Tian, Eric Mitchell, Huaxiu Yao, Christopher D Manning, and Chelsea Finn. 2023. Fine-tuning language models for factuality. arXiv preprint arXiv:2311.08401

    2023 arXiv

  12. [20]

    Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. 2022. Chain-of-thought prompting elicits reasoning in large language models. Advances in neural information processing systems, 35:24824--24837

    2022

  13. [21]

    Yuqing Yang, Ethan Chern, Xipeng Qiu, Graham Neubig, and Pengfei Liu. 2023. Alignment for honesty. arXiv preprint arXiv:2312.07000

    2023 arXiv

  14. [22]

    Shaolei Zhang, Tian Yu, and Yang Feng. 2024. Truthx: Alleviating hallucinations by editing large language models in truthful space. arXiv preprint arXiv:2402.17811

    2024 arXiv

  15. [23]

    Yue Zhang, Leyang Cui, Wei Bi, and Shuming Shi. 2023 a . Alleviating hallucinations of large language models through induced hallucinations. arXiv preprint arXiv:2312.15710

    2023 arXiv

  16. [24]

    Yue Zhang, Yafu Li, Leyang Cui, Deng Cai, Lemao Liu, Tingchen Fu, Xinting Huang, Enbo Zhao, Yu Zhang, Yulong Chen, et al. 2023 b . Siren's song in the ai ocean: a survey on hallucination in large language models. arXiv preprint arXiv:2309.01219

    2023 arXiv

  17. [25]

    Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, et al. 2023. A survey of large language models. arXiv preprint arXiv:2303.18223

    2023 arXiv

  18. [26]

    Chunting Zhou, Pengfei Liu, Puxin Xu, Srinivasan Iyer, Jiao Sun, Yuning Mao, Xuezhe Ma, Avia Efrat, Ping Yu, Lili Yu, et al. 2024. Lima: Less is more for alignment. Advances in Neural Information Processing Systems, 36

    2024

  19. [27]

    Andy Zou, Long Phan, Sarah Chen, James Campbell, Phillip Guo, Richard Ren, Alexander Pan, Xuwang Yin, Mantas Mazeika, Ann-Kathrin Dombrowski, et al. 2023. Representation engineering: A top-down approach to ai transparency. arXiv preprint arXiv:2310.01405

    2023 arXiv

  20. [28]

    online" 'onlinestring :=

    ENTRY address archivePrefix author booktitle chapter edition editor eid eprint eprinttype howpublished institution journal key month note number organization pages publisher school series title type volume year doi pubmed url lastchecked label extra.label sort.label short.list...

  21. [29]

    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 gl...

Pith tools

Reviewed August 9, 2026 · model on record in the stance chip above.