Furthest Reasoning with Plan Assessment: Stable Reasoning Path with
  Retrieval-Augmented Large Language Models
Yin Zhu Zhiling Luo Gong Cheng
Large Language Models (LLMs), acting as a powerful reasoner and generator,
exhibit extraordinary performance across various natural language tasks, such
as question answering (QA). Among these tasks, Multi-Hop Question Answering
(MHQA) stands as a widely discussed category, necessitating seamless
integration between LLMs and the retrieval of external knowledge. Existing
methods employ LLM to generate reasoning paths and plans, and utilize IR to
iteratively retrieve related knowledge, but these approaches have inherent
flaws. On one hand, Information Retriever (IR) is hindered by the low quality
of generated queries by LLM. On the other hand, LLM is easily misguided by the
irrelevant knowledge by IR. These inaccuracies, accumulated by the iterative
interaction between IR and LLM, lead to a disaster in effectiveness at the end.
To overcome above barriers, in this paper, we propose a novel pipeline for MHQA
called Furthest-Reasoning-with-Plan-Assessment (FuRePA), including an improved
framework (Furthest Reasoning) and an attached module (Plan Assessor). 1)
Furthest reasoning operates by masking previous reasoning path and generated
queries for LLM, encouraging LLM generating chain of thought from scratch in
each iteration. This approach enables LLM to break the shackle built by
previous misleading thoughts and queries (if any). 2) The Plan Assessor is a
trained evaluator that selects an appropriate plan from a group of candidate
plans proposed by LLM. Our methods are evaluated on three highly recognized
public multi-hop question answering datasets and outperform state-of-the-art on
most metrics (achieving a 10%-12% in answer accuracy).