mlsanityData to 7 Oct 2026

Learning from Mistakes via Cooperative Study Assistant for Large Language Models

arXiv 2305.13829 ↗First posted 23 May 2023cs.CLcited by 9

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Abstract (arXiv)

Large language models (LLMs) have demonstrated their potential to refine their generation based on their own feedback. However, the feedback from LLM itself is often inaccurate, thereby limiting its benefits. In this paper, we propose Study Assistant for Large LAnguage Model (SALAM), a novel framework with an auxiliary agent to assist the main LLM in learning from mistakes through interactive cooperation. In the gathering phase, the student assistant agent probes the main LLM, analyzes its errors, and collects the interaction in a mistake memory. During the examination phase, the study assistant provides guidelines by retrieving relevant cases to help the main LLM anticipate and avoid similar errors. We first investigate the effectiveness of a general study assistant and then customize it to provide LLM-specific guidance through imitation learning from successful guidance experiences. Our experiments on three LLMs using two challenging frameworks demonstrate that SALAM can significantly boost LLMs by an accuracy margin of up to 6.6 on BBH and 12.6 on BBQ.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 32 of its 39 references that are in the map. First posted on arXiv 23 May 2023.

Builds on

32 of 39 references.

References · 32

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
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20 more
  1. 2020Learning to summarize from human feedbackcited by 2,562
  2. 2022Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language modelscited by 1,970
  3. 2022Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Themcited by 1,767
  4. 2020Knowledge Distillation: A Surveycited by 1,578
  5. 2022Solving math word problems with process- and outcome-based feedbackcited by 871
  6. 2022GPT-NeoX-20B: An Open-Source Autoregressive Language Modelcited by 711
  7. 2021BBQ: A Hand-Built Bias Benchmark for Question Answeringcited by 683
  8. 2022Large Language Models Can Self-Improvecited by 656
  9. 2019Improved Knowledge Distillation via Teacher Assistantcited by 567
  10. 2023Large Language Models Are State-of-the-Art Evaluators of Translation Qualitycited by 416
  11. 2022Self-critiquing models for assisting human evaluatorscited by 372
  12. 2023Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedbackcited by 351
  13. 2022Teaching Small Language Models to Reasoncited by 279
  14. 2022Generating Sequences by Learning to Self-Correctcited by 271
  15. 2021Symbolic Knowledge Distillation: from General Language Models to Commonsense Modelscited by 270
  16. 2022Quark: Controllable Text Generation with Reinforced Unlearningcited by 236
  17. 2022Re3: Generating Longer Stories With Recursive Reprompting and Revisioncited by 193
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  19. 2023The Wisdom of Hindsight Makes Language Models Better Instruction Followerscited by 59
  20. 2022I2D2: Inductive Knowledge Distillation with NeuroLogic and Self-Imitationcited by 21

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 2; 2026: 4.

9 papers.

  1. 2024Recover: A Neuro-Symbolic Framework for Failure Detection and Recoverycited by 15
  2. 2024Beyond Imitation: Learning Key Reasoning Steps from Dual Chain-of-Thoughts in Reasoning Distillationcited by 13
  3. 2024TPD: Enhancing Student Language Model Reasoning via Principle Discovery and Guidancecited by 5
  4. 2026Reward Prediction with Factorized World Statescited by 2
  5. 2026Learning-Zone Energy: Online Data Selection for Efficient RL Post-Trainingcited by 2
  6. 2025It Helps to Take a Second Opinion: Teaching Smaller LLMs to Deliberate Mutually via Selective Rationale Optimisationcited by 1
  7. 2025Error-Driven Scene Editing for 3D Grounding in Large Language Modelscited by 0
  8. 2026GRPO and Reflection Reward for Mathematical Reasoning in Large Language Modelscited by 0
  9. 2026Smarter by the Moment: Environment-Driven Dynamic Policies for Continual LLM Improvementcited by 0