Papers with long-form question-answering
Multi-Review Fusion-in-Context (2024.findings-naacl)
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| Challenge: | Current methods for generating text are opaque and difficult to control and interpret due to their opaque nature. |
| Approach: | They propose a modular approach with separate components for each step . they formalize Fusion-in-Context as a standalone task, whose input consists of source texts with highlighted spans of targeted content. |
| Outcome: | The proposed approach is based on a curated dataset of 1000 instances in the reviews domain and a novel evaluation framework for assessing the faithfulness and coverage of highlights. |
A Novel Computational Modeling Foundation for Automatic Coherence Assessment (2025.naacl-long)
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| Challenge: | Existing models for text coherence assessment rely on a proxy task . however, this approach does not capture the full range of factors contributing to coherency. |
| Approach: | They propose a formal linguistic definition of what makes a discourse coherent and formalize these conditions as respective computational tasks that are jointly trained. |
| Outcome: | The proposed model improves on two human-rated coherence benchmarks. |
Learning to Plan and Generate Text with Citations (2024.acl-long)
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Constanza Fierro, Reinald Kim Amplayo, Fantine Huot, Nicola De Cao, Joshua Maynez, Shashi Narayan, Mirella Lapata
| Challenge: | Large language models (LLMs) are increasingly useful in information-seeking scenarios, ranging from answering simple questions to generating responses to search-like queries. |
| Approach: | They propose to use plan-based models to improve faithfulness, grounding, and controllability of generated content and its organization. |
| Outcome: | The proposed models improve faithfulness, grounding, and controllability of generated content and its organization. |
Improving Contextual Faithfulness of Large Language Models via Retrieval Heads-Induced Optimization (2025.acl-long)
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Lei Huang, Xiaocheng Feng, Weitao Ma, Yuchun Fan, Xiachong Feng, Yangfan Ye, Weihong Zhong, Yuxuan Gu, Baoxin Wang, Dayong Wu, Guoping Hu, Bing Qin
| Challenge: | Existing frameworks for retrieval-augmented large language models (LLMs) are lacking in LFQA faithfulness testing. |
| Approach: | They propose a framework to teach retrieval-augmented large language models to explicitly discriminate between faithful and unfaithful generations. |
| Outcome: | The proposed framework outperforms GPT-4o in LFQA scenarios and outperformed existing benchmarks. |