Papers with open-domain question answering datasets
Chain-of-Rewrite: Aligning Question and Documents for Open-Domain Question Answering (2024.findings-emnlp)
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Chunlei Xin, Yaojie Lu, Hongyu Lin, Shuheng Zhou, Huijia Zhu, Weiqiang Wang, Zhongyi Liu, Xianpei Han, Le Sun
| Challenge: | Existing approaches to answer open-domain question have encountered term mismatch and limited interaction between IR systems and large language models. |
| Approach: | They propose a method which leverages the guidance and feedback gained from the analysis to provide faithful and consistent extensions for effective question answering. |
| Outcome: | Experiments on four open-domain question answering datasets show the proposed method performs well under zero-shot settings. |
Entropy-Based Decoding for Retrieval-Augmented Large Language Models (2025.naacl-long)
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| Challenge: | Despite their success, retrieval-augmented LLMs still face the distractibility issue, where the generated responses are negatively influenced by noise from both external and intrinsic knowledge sources. |
| Approach: | They propose a entropy-based document-parallel ensemble decoding method that prioritizes low-entropies from retrieved documents and incorporates a contrastive decoding mechanism that contrasts the obtained low- and high-entropic ensemble distributions with the high-end internal knowledge across layers. |
| Outcome: | The proposed method improves on open-domain question answering datasets and shows that it is highly efficient. |
Large Language Models Can Self-Correct with Key Condition Verification (2024.emnlp-main)
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| Challenge: | Existing methods to correct reasoning without external feedback have not been used in large language models. |
| Approach: | They propose an iterative verify-then-correct framework to progressively identify and correct (probably) false responses, named ProCo. |
| Outcome: | The proposed method improves the accuracy of LLMs on three reasoning tasks. |