Related papers: An Analysis of Decoding Methods for LLM-based Agents for Faithful Multi-Hop Question Answering

An Analysis of Decoding Methods for LLM-based Agents for Faithful Multi-Hop Question Answering

URL: http://arxiv.org/abs/2503.23415v1
Date: Sun, 30 Mar 2025 12:18:21 GMT
Title: An Analysis of Decoding Methods for LLM-based Agents for Faithful Multi-Hop Question Answering
Authors: Alexander Murphy, Mohd Sanad Zaki Rizvi, Aden Haussmann, Ping Nie, Guifu Liu, Aryo Pradipta Gema, Pasquale Minervini,
Abstract summary: Large Language Models (LLMs) frequently produce factually inaccurate outputs.<n>This phenomenon limits their accuracy in knowledge-intensive NLP tasks.<n>Recent research has explored training-free decoding strategies to improve the faithfulness of model generations.
Score: 44.41915467956464
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Large Language Models (LLMs) frequently produce factually inaccurate outputs - a phenomenon known as hallucination - which limits their accuracy in knowledge-intensive NLP tasks. Retrieval-augmented generation and agentic frameworks such as Reasoning and Acting (ReAct) can address this issue by giving the model access to external knowledge. However, LLMs often fail to remain faithful to retrieved information. Mitigating this is critical, especially if LLMs are required to reason about the retrieved information. Recent research has explored training-free decoding strategies to improve the faithfulness of model generations. We present a systematic analysis of how the combination of the ReAct framework and decoding strategies (i.e., DeCoRe, DoLa, and CAD) can influence the faithfulness of LLM-generated answers. Our results show that combining an agentic framework for knowledge retrieval with decoding methods that enhance faithfulness can increase accuracy on the downstream Multi-Hop Question Answering tasks. For example, we observe an F1 increase from 19.5 to 32.6 on HotpotQA when using ReAct and DoLa.

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