Papers with Critique
LLM Self-Correction with DeCRIM: Decompose, Critique, and Refine for Enhanced Following of Instructions with Multiple Constraints (2024.findings-emnlp)
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Thomas Palmeira Ferraz, Kartik Mehta, Yu-Hsiang Lin, Haw-Shiuan Chang, Shereen Oraby, Sijia Liu, Vivek Subramanian, Tagyoung Chung, Mohit Bansal, Nanyun Peng
| Challenge: | Recent studies have shown that LLMs struggle with instructions containing multiple constraints. |
| Approach: | They propose a self-correction pipeline that decomposes the original instruction into a list of constraints and uses a Critic model to decide when and where the LLM’s response needs refinement. |
| Outcome: | The proposed model outperforms GPT-4 on RealInstruct and IFEval even with weak feedback. |
MCQG-SRefine: Multiple Choice Question Generation and Evaluation with Iterative Self-Critique, Correction, and Comparison Feedback (2025.naacl-long)
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| Challenge: | Generating multiple-choice questions (MCQG) for professional exams is challenging due to outdated knowledge, hallucination issues, and prompt sensitivity. |
| Approach: | They propose a framework for converting medical cases into high-quality USMLE-style questions using a self-refine-based framework. |
| Outcome: | The proposed framework improves human expert satisfaction regarding quality and difficulty of medical questions. |
The Critique of Critique (2024.findings-acl)
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| Challenge: | MetaCritique builds specific quantification criteria to evaluate the quality of critique . a systematic method to evaluate critique is lacking. |
| Approach: | They propose a critique of critique, termed MetaCritique, which builds specific quantification criteria and aggregates each AIU's judgment for the overall score. |
| Outcome: | The proposed method can achieve near-human performance across 16 datasets. |
Learning to Refine with Fine-Grained Natural Language Feedback (2024.findings-emnlp)
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| Challenge: | Recent work has explored the capability of large language models to identify and correct errors in LLM-generated responses. |
| Approach: | They propose to combine refinement with feedback into three distinct competencies . step 1: Detect, Critique, Refine gives a fine-grained feedback about errors . |
| Outcome: | The proposed method outperforms existing refinement approaches and models not fine-tuned for factuality critiquing. |