Papers with Teaching large language models
Smurfs: Multi-Agent System using Context-Efficient DFSDT for Tool Planning (2025.naacl-long)
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| Challenge: | Teaching large language models to use tools for solving complex problems can grant them human-like reasoning abilities. |
| Approach: | They propose a multi-agent system that enhances the Deep First Search Decision Tree (DFSDT) to address issues like error propagation and limited exploration in ReAct . |
| Outcome: | The proposed system reduces token usage by 60.9% compared to existing methods and performs on par with GPT-4-DFSDT. |
Towards Improved Multi-Source Attribution for Long-Form Answer Generation (2024.naacl-long)
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| Challenge: | Current LLMs struggle with attribution for long-form answers which require reasoning over multiple evidence sources. |
| Approach: | They propose to improve attribution capability of large language models for long-form answer generation to multiple sources with multiple citations per sentence. |
| Outcome: | The proposed model improves on a wide range of attribution benchmark datasets on PolitiICite, a multi-source attribution dataset based on PolitIcite articles . |
Advancing Large Language Model Attribution through Self-Improving (2024.emnlp-main)
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Lei Huang, Xiaocheng Feng, Weitao Ma, Liang Zhao, Yuchun Fan, Weihong Zhong, Dongliang Xu, Qing Yang, Hongtao Liu, Bing Qin
| Challenge: | Teaching large language models to generate text with citations to evidence sources requires high-quality attribution data, which is costly and labor-intensive. |
| Approach: | They propose a framework for iteratively improving the attribution capability of large language models (LLMs) by attributing output to verifiable sources. |
| Outcome: | Experiments on three open-domain question-answering datasets show that START improves in aggregating information across multiple sources. |