Learning to Plan for Retrieval-Augmented Large Language Models from Knowledge Graphs

• URL: http://arxiv.org/abs/2406.14282v3
• Date: Wed, 23 Oct 2024 09:42:59 GMT
• Title: Learning to Plan for Retrieval-Augmented Large Language Models from Knowledge Graphs
• Authors: Junjie Wang, Mingyang Chen, Binbin Hu, Dan Yang, Ziqi Liu, Yue Shen, Peng Wei, Zhiqiang Zhang, Jinjie Gu, Jun Zhou, Jeff Z. Pan, Wen Zhang, Huajun Chen,
• Abstract summary: We introduce a novel framework for enhancing large language models' (LLMs) planning capabilities by using planning data derived from knowledge graphs (KGs) LLMs fine-tuned with KG data have improved planning capabilities, better equipping them to handle complex QA tasks that involve retrieval.
• Score: 59.76268575344119
• License:
• Abstract: Improving the performance of large language models (LLMs) in complex question-answering (QA) scenarios has always been a research focal point. Recent studies have attempted to enhance LLMs' performance by combining step-wise planning with external retrieval. While effective for advanced models like GPT-3.5, smaller LLMs face challenges in decomposing complex questions, necessitating supervised fine-tuning. Previous work has relied on manual annotation and knowledge distillation from teacher LLMs, which are time-consuming and not accurate enough. In this paper, we introduce a novel framework for enhancing LLMs' planning capabilities by using planning data derived from knowledge graphs (KGs). LLMs fine-tuned with this data have improved planning capabilities, better equipping them to handle complex QA tasks that involve retrieval. Evaluations on multiple datasets, including our newly proposed benchmark, highlight the effectiveness of our framework and the benefits of KG-derived planning data.

Related papers

• Non-myopic Generation of Language Models for Reasoning and Planning [45.75146679449453]
  This paper proposes a novel method, Predictive-Decoding, that leverages Model Predictive Control to enhance planning accuracy. Our experiments show significant improvements in a wide range of tasks for math, coding, and agents.
  arXiv  Detail & Related papers  (2024-10-22T17:13:38Z)
• Learning to Reduce: Towards Improving Performance of Large Language Models on Structured Data [39.29778853025738]
  Large Language Models (LLMs) have been achieving competent performance on a wide range of downstream tasks. This paper proposes a framework, Learning to Reduce, that fine-tunes a language model with On-Policy Learning to generate a reduced version of an input structured data.
  arXiv  Detail & Related papers  (2024-07-03T01:51:50Z)
• Retrieve-Plan-Generation: An Iterative Planning and Answering Framework for Knowledge-Intensive LLM Generation [47.22520829950929]
  We propose the Retrieve-Plan-Generation (RPG) framework for large language models (LLMs) RPG generates plan tokens to guide subsequent generation in the plan stage. In the answer stage, the model selects relevant fine-grained paragraphs based on the plan and uses them for further answer generation.
  arXiv  Detail & Related papers  (2024-06-21T08:45:52Z)
• Q*: Improving Multi-step Reasoning for LLMs with Deliberative Planning [53.6472920229013]
  Large Language Models (LLMs) have demonstrated impressive capability in many natural language tasks. LLMs are prone to produce errors, hallucinations and inconsistent statements when performing multi-step reasoning. We introduce Q*, a framework for guiding LLMs decoding process with deliberative planning.
  arXiv  Detail & Related papers  (2024-06-20T13:08:09Z)
• Exploring and Benchmarking the Planning Capabilities of Large Language Models [57.23454975238014]
  This work lays the foundations for improving planning capabilities of large language models (LLMs) We construct a comprehensive benchmark suite encompassing both classical planning benchmarks and natural language scenarios. We investigate the use of many-shot in-context learning to enhance LLM planning, exploring the relationship between increased context length and improved planning performance.
  arXiv  Detail & Related papers  (2024-06-18T22:57:06Z)
• Large Language Models are Learnable Planners for Long-Term Recommendation [59.167795967630305]
  Planning for both immediate and long-term benefits becomes increasingly important in recommendation. Existing methods apply Reinforcement Learning to learn planning capacity by maximizing cumulative reward for long-term recommendation. We propose to leverage the remarkable planning capabilities over sparse data of Large Language Models for long-term recommendation.
  arXiv  Detail & Related papers  (2024-02-29T13:49:56Z)
• Understanding the planning of LLM agents: A survey [98.82513390811148]
  This survey provides the first systematic view of LLM-based agents planning, covering recent works aiming to improve planning ability. Comprehensive analyses are conducted for each direction, and further challenges in the field of research are discussed.
  arXiv  Detail & Related papers  (2024-02-05T04:25:24Z)
• Integrating Graphs with Large Language Models: Methods and Prospects [68.37584693537555]
  Large language models (LLMs) have emerged as frontrunners, showcasing unparalleled prowess in diverse applications. Merging the capabilities of LLMs with graph-structured data has been a topic of keen interest. This paper bifurcates such integrations into two predominant categories.
  arXiv  Detail & Related papers  (2023-10-09T07:59:34Z)
• Understanding the Capabilities of Large Language Models for Automated Planning [24.37599752610625]
  The study seeks to shed light on the capabilities of LLMs in solving complex planning problems. It provides insights into the most effective approaches for using LLMs in this context.
  arXiv  Detail & Related papers  (2023-05-25T15:21:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.