Can LLMs Learn from Previous Mistakes? Investigating LLMs’ Errors to Boost for Reasoning (2024.acl-long)
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| Challenge: | Recent studies have shown the benefits to LLMs from fine-tuning golden-standard Chain-of-Thought rationales or using them as correct examples in few-shot prompting. |
| Approach: | They propose a new benchmark to test the effectiveness of large language models by leveraging errors to enhance reasoning capabilities. |
| Outcome: | The proposed methods can be used to fine-tune models in correct and incorrect domains, rather than tuning models to learn ground truth in traditional methods. |
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| Challenge: | Existing work has shown that simple learning can enhance the chain-of-thought (CoT) reasoning of large language models. |
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No Need for Explanations: LLMs can implicitly learn from mistakes in-context (2025.emnlp-main)
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| Challenge: | Existing literature assumes that correct answers to large language models must be accompanied by comprehensive rationales to be helpful. |
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LLMs cannot find reasoning errors, but can correct them given the error location (2024.findings-acl)
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| Challenge: | Recent attempts to self-correct logical or reasoning errors often cause correct answers to become incorrect, resulting in poor performance overall. |
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I Learn Better If You Speak My Language: Understanding the Superior Performance of Fine-Tuning Large Language Models with LLM-Generated Responses (2024.emnlp-main)
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| Challenge: | Recent research has demonstrated that a large language model (LLM) can generate training data for another LLM, or for creating supplementary training materials, such as rationales. |
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On the Impact of Fine-Tuning on Chain-of-Thought Reasoning (2025.naacl-long)
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| Challenge: | Large language models have emerged as powerful tools for general intelligence, showcasing advanced natural language processing capabilities. |
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| Challenge: | Large Language Models (LLMs) have enabled powerful domain-specific applications through supervised fine-tuning. |
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| Challenge: | Existing work has focused on relatively complex “many-hop” reasoning problems. |
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Current Advances in LLM Reasoning (2026.acl-tutorials)
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| Challenge: | This tutorial examines comprehensive evaluation strategies to assess the reasoning abilities of large language models (LLMs) advanced inference time methods and post-training methods that aim to make LLMs think more like humans are discussed in this tutorial. |
| Approach: | This tutorial explores comprehensive evaluation strategies to assess the reasoning abilities of large language models (LLMs) and discusses two types of methods to improve models’ reasoning: advanced inference time methods, structured and self-improvement inference methods, and post-training methods, such as RLHF, DPO, and GRPO. |
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| Challenge: | Large Language Models (LLMs) have demonstrated impressive capabilities in reasoning, but how they propagate within their reasoning process remains underexplored. |
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Exposing the Achilles’ Heel: Evaluating LLMs Ability to Handle Mistakes in Mathematical Reasoning (2025.acl-long)
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| Challenge: | Existing evaluations focus on final accuracy, neglecting the critical aspect of reasoning capabilities. |
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