Yanming Liu
2024
RA-ISF: Learning to Answer and Understand from Retrieval Augmentation via Iterative Self-Feedback
Yanming Liu
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Xinyue Peng
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Xuhong Zhang
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Weihao Liu
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Jianwei Yin
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Jiannan Cao
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Tianyu Du
Findings of the Association for Computational Linguistics ACL 2024
Large language models (LLMs) demonstrate exceptional performance in numerous tasks but still heavily rely on knowledge stored in their parameters. Moreover, updating this knowledge incurs high training costs. Retrieval-augmented generation (RAG) methods address this issue by integrating external knowledge. The model can answer questions it couldn’t previously by retrieving knowledge relevant to the query. This approach improves performance in certain scenarios for specific tasks. However, if irrelevant texts are retrieved, it may impair model performance. In this paper, we propose Retrieval Augmented Iterative Self-Feedback (RA-ISF), a framework that iteratively decomposes tasks and processes them in three submodules to enhance the model’s problem-solving capabilities. Experiments show that our method outperforms existing benchmarks, performing well on models like GPT3.5, Llama2, significantly enhancing factual reasoning capabilities and reducing hallucinations.
ERA-CoT: Improving Chain-of-Thought through Entity Relationship Analysis
Yanming Liu
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Xinyue Peng
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Tianyu Du
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Jianwei Yin
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Weihao Liu
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Xuhong Zhang
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Large language models (LLMs) have achieved commendable accomplishments in various natural language processing tasks. However, LLMs still encounter significant challenges when dealing with complex scenarios involving multiple entities. These challenges arise from the presence of implicit relationships that demand multi-step reasoning. In this paper, we propose a novel approach ERA-CoT, which aids LLMs in understanding context by capturing relationships between entities and supports the reasoning of diverse tasks through Chain-of-Thoughts (CoT).Experimental results show that ERA-CoT demonstrates the superior performance of our proposed method compared to current CoT prompting methods, achieving a significant improvement of an average of 5.1% on GPT3.5 compared to previous SOTA baselines. Our analysis indicates that ERA-CoT increases the LLM’s understanding of entity relationships, significantly improves the accuracy of question answering, and enhances the reasoning ability of LLMs.
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Co-authors
- Xinyue Peng 2
- Xuhong Zhang 2
- Weihao Liu 2
- Jianwei Yin 2
- Tianyu Du 2
- show all...