Pith. sign in

REVIEW 4 major objections 4 minor 26 references

Theorem-of-Thought: A Multi-Agent Framework for Abductive, Deductive, and Inductive Reasoning in Language Models

T0 review · 4 major / 4 minor · reviewed 2026-08-07 · deepseek-v4-flash

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

Pith's one-line read This paper claims that a three-agent, three-inference-mode framework with coherence-scored reasoning graphs beats chain-of-thought, self-consistency, and chain-of-thought decoding on the WebOfLies and MultiArith benchmarks across multiple…

desk verdict A well-specified multi-agent reasoning framework with a novel NLI-based graph scorer, but the empirical claims outrun the evidence; should be reviewed with revision. read the letter →

arxiv 2506.07106 v2 pith:3EWMDV3S submitted 2025-06-08 cs.CL cs.AI

Samir Abdaljalil , Hasan Kurban , Khalid Qaraqe , Erchin Serpedin This is my paper
classification cs.CLcs.AI
keywords Theorem-of-Thoughtmulti-agentreasoningabductivedeductiveinductivebeliefpropagationnaturallanguageinferencechain-of-thought
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

Theorem-of-Thought (ToTh) tries to establish that an LLM can reason more reliably when three parallel agents, each using one classical inference style (abductive, deductive, or inductive), produce separate reasoning traces that are turned into graphs and scored for internal coherence using NLI-calibrated Bayesian belief propagation. The most coherent graph then contributes its final node as the answer. On the symbolic WebOfLies and numerical MultiArith benchmarks, the paper reports that this pipeline consistently outperforms chain-of-thought, self-consistency, and chain-of-thought decoding on Mistral-7B and DeepSeek-7B, while remaining within three percentage points of the best baseline on Phi-3.5-mini. If true, this matters because it suggests a prompt-only, verification-based route to more robust and interpretable LLM reasoning that avoids the cost of sampling many decoding passes.

What carries the argument

The load-bearing object is the Formal Reasoning Graph (FRG) with its coherence score: a directed graph whose nodes are reasoning steps and whose edges carry NLI-derived trust scores, through which beliefs initialized at 0.5 are propagated via the Bayesian update $f(p, \theta) = \frac{p\theta}{p\theta + (1-p)(1-\theta)}$, with multi-parent nodes averaged. The final graph score is $Score(G) = \mu - H$, the mean node belief minus the normalized binary entropy, and the graph with the highest score contributes its last node as the answer. This mechanism is what converts three parallel guesses into a single, internally consistent chain that the paper argues is more reliable than linear prompting or majority voting.

What would settle it

An experiment that would settle the claim: take the same three agent traces, but instead of selecting by the coherence score, select the final answer at random or always choose a fixed agent; if accuracy stays the same, the coherence score is not doing the verification work the paper attributes to it. A more direct check is to measure the correlation between $Score(G)$ and whether the terminal node's answer is correct on a held-out sample; a correlation at or below zero would contradict the mechanism.

Watch

Extended reading notes

Core claim

The paper's central claim is that separating reasoning into the three classical inference modes, structuring each trace as a formal reasoning graph, and then selecting the graph with the highest coherence score is what drives accuracy, rather than the mere diversity of three attempts. Each graph edge carries an NLI-derived trust score (0.95 for entailment, 0.60 for neutral, 0.10 for contradiction), and beliefs initialized at 0.5 are propagated through the graph with a Bayesian update rule. Each graph is scored as the mean node belief minus the normalized binary entropy, and the highest-scoring graph's terminal node is taken as the final answer. The paper reports consistent gains over chain-of-thought, self-consistency, and chain-of-thought decoding on both evaluation benchmarks for two of the three tested models, with improvements up to 29 percentage points over CoT-Greedy on WebOfLies.

Load-bearing premise

The whole method leans on the belief that a reasoning trace with higher average confidence and lower entropy is the one most likely to hold the correct answer; if the coherence score does not actually pick out the correct agent's chain, the reported gains could come from the diversity of three attempts rather than from the verification step.

Editorial extensions

If this is right

  • ToTh provides accuracy gains over CoT, Self-Consistency, and CoT-Decoding on both tasks for Mistral-7B and DeepSeek-7B, and stays within 3% of the best method on Phi-3.5-mini.
  • The method runs in $O(k \cdot s)$ time, linear in the three agents and the trace length, making it cheaper than sampling-based approaches like Self-Consistency's 20 decoding passes.
  • ToTh's advantage grows with task difficulty: on the hardest WebOfLies setting (5 statements) it reaches 43% vs. 19% for CoT-Greedy and 38% for Self-Consistency.
  • The output is a structured, scored graph of reasoning steps, giving an explicit checkable record of how the final answer was derived.
  • The framework works on released open models without fine-tuning, relying only on styled prompts and a pretrained NLI model for scoring.

Reading between the lines

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

  • If the coherence score genuinely identifies the correct reasoning chain, the same NLI-plus-belief-propagation scoring could rank candidate chains produced by a single model, without needing three separately styled agents.
  • A stress test the paper does not run is comparing ToTh against randomly picking one of the three agents' answers: that comparison would reveal how much of the gain comes from the coherence score versus from the diversity of three independent attempts.
  • On ambiguous or creative prompts, the fixed abductive/deductive/inductive scaffold may hurt, since the paper's own limitation note says hybrid reasoning styles are not accommodated; a context-sensitive agent router could extend the framework.
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 Theorem-of-Thought (ToTh), a multi-agent framework in which three LLM agents reason in abductive, deductive, and inductive styles. Each agent's trace is converted into a formal reasoning graph whose edges are assigned NLI-based trust scores (0.95/0.60/0.10). Beliefs are propagated over the graph with a recursive Bayesian update, and a composite score (mean confidence minus normalized binary entropy) selects the graph whose terminal node provides the final answer. The method is evaluated on WebOfLies and MultiArith with Mistral-7B, DeepSeek-7B, and Phi-3.5-mini, comparing against CoT, Self-Consistency, and CoT-Decoding. The authors report improvements on most settings and claim consistent outperformance across multiple LLMs.

Significance. If the central empirical claim were robust, ToTh would be a useful and interpretable contribution to structured LLM reasoning, combining three classical inference modes with a lightweight NLI-based verification step. The framework is clearly described, the code is publicly available, and the use of externally fixed NLI models (RoBERTa-MNLI) avoids fitting-to-data circularity. However, the significance is limited by three load-bearing weaknesses: the headline claim is contradicted by some of the paper's own results, no statistical support is provided for the reported gains, and the graph-scoring mechanism is never validated as a better-than-random selector of correct answers.

major comments (4)
  1. [Abstract; Section 5.1; Table 1] The claim that ToTh 'consistently outperforms CoT, Self-Consistency, and CoT-Decoding across multiple LLMs' is contradicted by the paper's own results. Section 5.1 reports that on Phi-3.5-mini, CoT-Decoding reaches 99% on WebOfLies while ToTh reaches 96%, which the text itself describes as 'slightly lower performance.' Table 1 further shows CoT-Decoding beating ToTh on the 5-statement WebOfLies split (46% vs 43%) and on MultiArith d2/l3 (24% vs 21%). The abstract and conclusion should be revised to a qualified claim that explicitly reports these exceptions, for example 'generally improves or matches strong baselines on most settings.'
  2. [Section 4 'Experimental Setup'; Section 5] All experiments use fixed seeds and a single decoding pass per input, and the paper reports no error bars, confidence intervals, or significance tests. Given that the reported differences are often only a few percentage points (e.g., 43% vs 46% in Table 1), the claimed gains on Mistral-7B and DeepSeek-7B cannot be distinguished from sampling noise. The authors should report variance over multiple seeds per condition and run a significance test for the headline comparisons; this is essential to support the central 'consistently outperforms' claim.
  3. [Section 3 'Graph Scoring'] The final-answer mechanism is never validated. The paper does not test whether the graph with the highest Score(G) is more likely than a randomly selected agent's graph to contain the correct terminal node, nor does it compare against an oracle selection that picks the correct agent. Since the final answer is entirely determined by the selected graph, this is load-bearing: without such an ablation or correlation analysis, the observed gains could be explained simply by having three diverse attempts and picking one, rather than by the coherence-based selection mechanism. I would like to see an analysis of selection accuracy versus chance, and an ablation in which the final answer is chosen uniformly at random among the three agents.
  4. [Section 3 'Theoretical Complexity'] The complexity comparison is misleading. The paper states that ToTh executes 'a single structured reasoning pass per agent' and that its end-to-end complexity is O(k·s), compared with O(n) decoding passes for Self-Consistency and CoT-Decoding. However, with k=3 agents, ToTh requires three LLM decoding passes before the graph scoring begins, so the efficiency comparison should count LLM calls and decoding passes for all methods on the same footing. The O(k·s) analysis counts only graph operations and NLI evaluations, not the three generations that precede them.
minor comments (4)
  1. [Section 5.1; Table 1] The baseline name is inconsistent: the text refers to 'CoT-Greedy' while Table 1 uses 'CoT-G'; please define the abbreviation once and use it consistently in the text, figures, and tables.
  2. [Section 3] The notation P(v_c) is used both for the prior P(v) and the posterior belief after propagation; this makes equations (2)–(4) harder to follow. Please distinguish prior and posterior notations explicitly.
  3. [Section 5.2; Table 1] The column headers 'd0/l3', 'd0/l4', and 'd2/l3' are defined only in the table caption; consider repeating a one-line definition in the main text where these settings are first mentioned.
  4. [Limitations] The Limitations section acknowledges fixed reasoning types and propagation sensitivity, but does not acknowledge the lack of multi-seed variance or the lack of validation of the graph-selection mechanism; these should be added to the limitations discussion.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: ToTh's central empirical claim is tested against external benchmarks, its NLI-based scoring uses a fixed external model with hand-set trust score constants, and the only self-citation is non-load-bearing.

full rationale

The paper's derivation chain does not reduce to its inputs by construction. The final-answer mechanism selects among three agent graphs using Score(G^(i)) = mu^(i) - H^(i), where node beliefs are propagated from trust scores theta_uv = 0.95/0.60/0.10 assigned by RoBERTa-MNLI, a pretrained external NLI model. These constants are hand-set, not fitted to WebOfLies or MultiArith answers, so the reported predictions are not statistically forced by a fitted parameter. The benchmark evaluations in Sections 4 and 5 are external, and the baselines are standard methods (CoT, Self-Consistency, CoT-Decoding). The only self-citation is the passing reference to Abdaljalil et al. (2025) in the introduction for the claim that LLMs are susceptible to hallucinations; that citation is not the premise of any derivation, does not forbid alternative frameworks, and is not needed to establish ToTh's empirical results. The paper's own admission that CoT-Decoding 'marginally surpasses' ToTh on Phi-3.5 Mini, together with the absence of error bars and significance tests, is an accuracy-robustness concern rather than circularity: it weakens the 'consistently outperforms' claim but does not make any result equivalent to its inputs. The untested assumption that the graph-scoring coherence proxy selects the correct graph is a validity risk, not a circularity, because the score is not defined in terms of the answer label and is not trained on the target data. The Limitations section's acknowledged fixed reasoning types and propagation sensitivity are stated limitations, not circular steps.

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

The framework rests on several unvalidated modeling choices: hand-set trust scores, a Bayesian update with averaging over parents, a graph-scoring heuristic, and the assumption that NLI labels track logical support. None of these are derived or ablated, so they are carried as free parameters or domain assumptions.

free parameters (3)
  • NLI trust scores = entailment 0.95, neutral 0.60, contradiction 0.10
    Hand-set constants in Section 3 (Formal Reasoning Graph Construction) that determine how much weight each edge gets in belief propagation; no sensitivity analysis.
  • Prior node confidence = 0.5
    Initial belief for every node in Section 3 (Bayesian Confidence Propagation); maximum-entropy choice, not data-derived.
  • Scoring coefficients = 1 and -1
    Graph score is mean confidence minus normalized binary entropy, with equal weights; a design choice without ablation (Section 3 Graph Scoring).
assumptions (4)
  • domain assumption The NLI model's entailment/neutral/contradiction labels are a reliable proxy for logical support between reasoning steps.
    RoBERTa-MNLI is applied to adjacent reasoning steps; used to set edge trust scores. No validation that NLI correlates with logical validity on these tasks.
  • domain assumption Prompting one LLM with three different reasoning styles produces sufficiently diverse and useful reasoning traces.
    The whole multi-agent design assumes each agent can actually enact the requested inference mode and that at least one will be correct.
  • ad hoc to paper The recursive Bayesian update, with multiple parents averaged, is an appropriate way to propagate confidence through the reasoning graph.
    Adapted from Pearl's belief propagation but simplified; the averaging over parents is introduced in Section 3 without justification.
  • domain assumption The terminal node of the selected graph contains the correct final answer.
    Answer extraction assumes the last step of the most-coherent trace is the response, which may not be grounded.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Theorem-of-Thought: A Multi-Agent Framework for Abductive, Deductive, and Inductive Reasoning in Language Models." pith.science (2026). https://pith.science/paper/3EWMDV3S

@misc{pith2026250607106,
  author       = {Pith},
  title        = {Pith review of: Theorem-of-Thought: A Multi-Agent Framework for Abductive, Deductive, and Inductive Reasoning in Language Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/3EWMDV3S}},
  note         = {Machine review of arXiv:2506.07106}
}
read the original abstract

Large language models (LLMs) have shown strong performance across natural language reasoning tasks, yet their reasoning processes remain brittle and difficult to interpret. Prompting techniques like Chain-of-Thought (CoT) enhance reliability by eliciting intermediate reasoning steps or aggregating multiple outputs. However, they lack mechanisms for enforcing logical structure and assessing internal coherence. We introduce Theorem-of-Thought (ToTh), a novel framework that models reasoning as collaboration among three parallel agents, each simulating a distinct mode of inference: abductive, deductive, and inductive. Each agent produces a reasoning trace, which is structured into a formal reasoning graph. To evaluate consistency, we apply Bayesian belief propagation guided by natural language inference (NLI), assigning confidence scores to each step. The most coherent graph is selected to derive the final answer. Experiments on symbolic (WebOfLies) and numerical (MultiArith) reasoning benchmarks show that ToTh consistently outperforms CoT, Self-Consistency, and CoT-Decoding across multiple LLMs, while producing interpretable and logically grounded reasoning chains. Our findings suggest a promising direction for building more robust and cognitively inspired LLM reasoning. The implementation is available at https://github.com/KurbanIntelligenceLab/theorem-of-thought.

Figures

Figures reproduced from arXiv: 2506.07106 by the authors.

Figure 1
Figure 1. Overview of the Theorem-of-Thought (ToTh) reasoning pipeline. A question is independently [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Accuracy (%) comparison across reasoning pipelines on two benchmark tasks ( [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

26 extracted references · 12 canonical work pages

  1. [1]

    Samir Abdaljalil, Hasan Kurban, Parichit Sharma, Erchin Serpedin, and Rachad Atat. 2025. https://arxiv.org/abs/2503.05980 Sindex: Semantic inconsistency index for hallucination detection in llms . Preprint, arXiv:2503.05980

    arXiv 2025

  2. [2]

    Marah Abdin, Jyoti Aneja, Hany Awadalla, Ahmed Awadallah, Ammar Ahmad Awan, Nguyen Bach, Amit Bahree, Arash Bakhtiari, Jianmin Bao, Harkirat Behl, Alon Benhaim, Misha Bilenko, Johan Bjorck, Sébastien Bubeck, Martin Cai, Qin Cai, Vishrav Chaudhary, Dong Chen, Dongdong Chen, and 110 others. 2024. https://arxiv.org/abs/2404.14219 Phi-3 technical report: A hi...

    arXiv 2024

  3. [3]

    Zeyuan Allen-Zhu and Yuanzhi Li. 2025. https://openreview.net/forum?id=oDbiL9CLoS Physics of language models: Part 3.2, knowledge manipulation . In Proceedings of the International Conference on Learning Representations (ICLR). ICLR 2025 Poster

    work page 2025

  4. [4]

    DeepSeek-AI, Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, Xiaokang Zhang, Xingkai Yu, Yu Wu, Z. F. Wu, Zhibin Gou, Zhihong Shao, Zhuoshu Li, Ziyi Gao, and 181 others. 2025. https://arxiv.org/abs/2501.12948 Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement lea...

    arXiv 2025

  5. [5]

    Shizhe Diao, Pengcheng Wang, Yong Lin, Rui Pan, Xiang Liu, and Tong Zhang. 2024. https://doi.org/10.18653/v1/2024.acl-long.73 Active prompting with chain-of-thought for large language models . In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1330--1350, Bangkok, Thailand. Association...

    doi:10.18653/v1/2024.acl-long.73 2024

  6. [6]

    Sebastian Farquhar, Jannik Kossen, Lorenz Kuhn, and Yarin Gal. 2024. https://doi.org/10.1038/s41586-024-07421-0 Detecting hallucinations in large language models using semantic entropy . Nature, 630(8017):625--630. © 2024. The Author(s)

    doi:10.1038/s41586-024-07421-0 2024

  7. [7]

    Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, and William El Sayed. 2023. https://arxiv.org/abs/2310.0...

    arXiv 2023

  8. [8]

    Huiyuan Lai and Malvina Nissim. 2024. https://doi.org/10.18653/v1/2024.acl-long.649 m C o T : Multilingual instruction tuning for reasoning consistency in language models . In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 12012--12026, Bangkok, Thailand. Association for Computational...

    doi:10.18653/v1/2024.acl-long.649 2024

Show all 26 references
  1. [9]

    Alisa Liu, Xiaochuang Han, Yizhong Wang, Yulia Tsvetkov, Yejin Choi, and Noah A. Smith. 2024. https://openreview.net/forum?id=dribhnhm1i Tuning language models by proxy . In Proceedings of the Conference on Language Modeling (COLM)

    2024

  2. [10]

    Chaoqun Liu, Qin Chao, Wenxuan Zhang, Xiaobao Wu, Boyang Li, Anh Tuan Luu, and Lidong Bing. 2025. https://aclanthology.org/2025.coling-main.251/ Zero-to-strong generalization: Eliciting strong capabilities of large language models iteratively without gold labels . In Proceedin...

    2025

  3. [11]

    Elita Lobo, Chirag Agarwal, and Himabindu Lakkaraju. 2025. https://aclanthology.org/2025.naacl-long.584/ On the impact of fine-tuning on chain-of-thought reasoning . In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computation...

    2025

  4. [12]

    Chitu Okoli. 2022. https://doi.org/10.2139/ssrn.3774317 Inductive, abductive and deductive theorizing . SSRN Electronic Journal, Forthcoming

    2022 doi

  5. [13]

    Judea Pearl. 1988. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA

    1988

  6. [14]

    Leonardo Ranaldi and Andre Freitas. 2024. https://doi.org/10.18653/v1/2024.emnlp-main.139 Self-refine instruction-tuning for aligning reasoning in language models . In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 2325--2347, Mia...

    2024 doi

  7. [15]

    Subhro Roy, Tim Vieira, and Dan Roth. 2015. https://doi.org/10.1162/tacl_a_00118 Reasoning about quantities in natural language . Transactions of the Association for Computational Linguistics, 3:1--13

    2015 doi

  8. [16]

    Suzanna Sia, David Mueller, and Kevin Duh. 2024. https://openreview.net/forum?id=LLuSjg59an Where does in-context learning happen in large language models? In Proceedings of the 2024 Conference on Neural Information Processing Systems (NeurIPS). NeurIPS 2024 Poster

    2024

  9. [17]

    Mirac Suzgun, Nathan Scales, Nathanael Sch \"a rli, Sebastian Gehrmann, Yi Tay, Hyung Won Chung, Aakanksha Chowdhery, Quoc Le, Ed Chi, Denny Zhou, and Jason Wei. 2023. https://doi.org/10.18653/v1/2023.findings-acl.824 Challenging BIG -bench tasks and whether chain-of-thought c...

    2023 doi

  10. [18]

    Rosario Uceda Sosa, Karthikeyan Natesan Ramamurthy, Maria Chang, and Moninder Singh. 2024. https://openreview.net/forum?id=oSG6qGkt1 Reasoning about concepts with llms: Inconsistencies abound . Conference on Language Models (COLM). Published: 10 Jul 2024, Last Modified: 26 Aug 2024

    2024

  11. [19]

    Qineng Wang, Zihao Wang, Ying Su, Hanghang Tong, and Yangqiu Song. 2024. https://doi.org/10.18653/v1/2024.acl-long.331 Rethinking the bounds of LLM reasoning: Are multi-agent discussions the key? In Proceedings of the 62nd Annual Meeting of the Association for Computational Li...

    2024 doi

  12. [20]

    Le, Ed H

    Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc V. Le, Ed H. Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. 2023. https://openreview.net/forum?id=1PL1NIMMrw Self-consistency improves chain of thought reasoning in language models . In The Eleventh International Conferen...

    2023

  13. [21]

    Xuezhi Wang and Denny Zhou. 2024. https://openreview.net/forum?id=4Zt7S0B0Jp Chain-of-thought reasoning without prompting . In Proceedings of the 2024 Conference on Neural Information Processing Systems (NeurIPS). NeurIPS 2024 Poster

    2024

  14. [22]

    Chi, Quoc V

    Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed H. Chi, Quoc V. Le, and Denny Zhou. 2022. Chain-of-thought prompting elicits reasoning in large language models. In Proceedings of the 36th International Conference on Neural Information Processi...

    2022

  15. [23]

    Griffiths, Yuan Cao, and Karthik Narasimhan

    Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L. Griffiths, Yuan Cao, and Karthik Narasimhan. 2023. Tree of thoughts: deliberate problem solving with large language models. In Proceedings of the 37th International Conference on Neural Information Processing Systems,...

    2023

  16. [24]

    Yao Yao, Zuchao Li, and Hai Zhao. 2024. https://doi.org/10.18653/v1/2024.findings-naacl.183 G o T : Effective graph-of-thought reasoning in language models . In Findings of the Association for Computational Linguistics: NAACL 2024, pages 2901--2921, Mexico City, Mexico. Associ...

    2024 doi

  17. [25]

    Shengyu Zhang, Linfeng Dong, Xiaoya Li, Sen Zhang, Xiaofei Sun, Shuhe Wang, Jiwei Li, Runyi Hu, Tianwei Zhang, Fei Wu, and Guoyin Wang. 2024. https://arxiv.org/abs/2308.10792 Instruction tuning for large language models: A survey . Preprint, arXiv:2308.10792

    2024

  18. [26]

    Zhuosheng Zhang, Aston Zhang, Mu Li, and Alex Smola. 2023. https://openreview.net/forum?id=5NTt8GFjUHkr Automatic chain of thought prompting in large language models . In Proceedings of International Conference on Learning Representations (ICLR). Poster

    2023

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.