Cross-Refine: Improving Natural Language Explanation Generation by Learning in Tandem (2025.coling-main)
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| Challenge: | Natural language explanations (NLEs) are vital for elucidating the reasoning behind large language model (LLM) decisions. |
| Approach: | They propose a role-modeling approach that employs two LLMs as generator and critic to generate and refine NLEs. |
| Outcome: | The proposed model outperforms self-refine and can perform with less powerful LLMs. |
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| Challenge: | Existing work has demonstrated that Large Language Models (LLMs) can self-critique and refine their initial outputs, but this capability remains unexplored for improving explanation faithfulness. |
| Approach: | They propose a framework that enables models to improve the faithfulness of their own explanations through an iterative critique and refinement process without external supervision. |
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The ART of LLM Refinement: Ask, Refine, and Trust (2024.naacl-long)
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Kumar Shridhar, Koustuv Sinha, Andrew Cohen, Tianlu Wang, Ping Yu, Ramakanth Pasunuru, Mrinmaya Sachan, Jason Weston, Asli Celikyilmaz
| Challenge: | Large Language Models (LLMs) have demonstrated remarkable generative abilities, but can they judge the quality of their own generations and self-improve? |
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Verification and Refinement of Natural Language Explanations through LLM-Symbolic Theorem Proving (2024.emnlp-main)
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| Challenge: | Existing methods for assessing the validity of explanations for NLI are time-consuming and prone to logical errors. |
| Approach: | They propose a framework that integrates Large Language Models and Theorem Provers to verify and refine natural language explanations through crowd-sourcing . they propose to use TPs to generate and formalise explanatory sentences and suggest potential inference strategies for NLI. |
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Enhancing Recommendation Explanations through User-Centric Refinement (2025.findings-emnlp)
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| Challenge: | Existing explanations for user reviews often fail to meet user-centric aspects, reducing their usefulness to users. |
| Approach: | They propose a paradigm that refines initial explanations generated by existing models during the inference stage to enhance their quality in multiple aspects. |
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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. |
Dual Inference for Improving Language Understanding and Generation (2020.findings-emnlp)
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| Challenge: | Existing studies have exploited the duality of the task pairs in machine translation and speech recognition. |
| Approach: | They propose to leverage the duality in the inference stage without retraining whole models. |
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Towards Consistent Natural-Language Explanations via Explanation-Consistency Finetuning (2025.coling-main)
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| Challenge: | Large language models generate convincing, fluent explanations, but they often generate inconsistent explanations on different inputs. |
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Rethinking Code Refinement: Learning to Judge Code Efficiency (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have shown impressive capabilities in understanding and generating codes. |
| Approach: | They propose a method that is trained to judge the efficiency between two different versions of code by either classifying the superior one or predicting the relative improvement. |
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Faithful and Robust LLM-Driven Theorem Proving for NLI Explanations (2025.acl-long)
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| Challenge: | Recent work has shown that the interaction of large language models (LLMs) with theorem provers (TPs) can help verify and improve the validity of NLI explanations. |
| Approach: | They propose to use logical expressions to guide LLMs in generating structured proof sketches and to use them to improve their accuracy. |
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What Does LLM Refinement Actually Improve? A Systematic Study on Document-Level Literary Translation (2026.acl-long)
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Shaomu Tan, Dawei Zhu, Ke Tran, Michael Denkowski, Sony Trenous, Leonardo F. R. Ribeiro, Bill Byrne, Felix Hieber
| Challenge: | Large language models (LLMs) have made document-level machine translation increasingly practical, enabled by long-context modeling and strong generation quality. |
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