# Improving Retrieval Augmented Language Model with Self-Reasoning

**Authors:** Yuan Xia, Jingbo Zhou, Zhenhui Shi, Jun Chen, Haifeng Huang  
**arXiv:** [2407.19813](https://arxiv.org/abs/2407.19813)  
**Date:** 2024-07-29  
**Summary page:** https://www.summarizepaper.com/en/paper/2407.19813v1/

## Key points

- The Retrieval-Augmented Language Model (RALM) is a powerful tool for knowledge-intensive tasks in natural language processing by incorporating external knowledge during inference.
- Challenges remain in implementing RALMs, particularly in terms of reliability and traceability.
- A novel self-reasoning framework has been proposed to enhance the reliability and traceability of RALMs by leveraging reasoning trajectories generated by the model itself.
- The framework involves three key processes: a relevance-aware process, an evidence-aware selective process, and a trajectory analysis process.
- Data generation and quality control are crucial for training RALMs, with GPT-4 utilized to generate training data for the relevance-aware process.

## Summary

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In the realm of natural language processing, the Retrieval-Augmented Language Model (RALM) has emerged as a powerful tool for knowledge-intensive tasks by incorporating external knowledge during inference. This integration helps to address the issue of factual hallucinations often found in large language models (LLMs). Despite its successes, challenges remain in implementing RALMs, particularly in terms of reliability and traceability. To tackle these challenges, a novel self-reasoning framework has been proposed to enhance the reliability and traceability of RALMs. The core idea behind this framework is to leverage reasoning trajectories generated by the LLM itself. This involves constructing self-reason trajectories through three key processes: a relevance-aware process, an evidence-aware selective process, and a trajectory analysis process. The evidence-aware selective process focuses on how people typically identify crucial sentences from provided documents when answering questions. In contrast, LLMs need to explicitly formulate their reasoning trajectories. In this process, the LLM selects key sentences from relevant documents and provides reasons why these snippets support the answer to a given question. These selected sentences are defined as evidence within the framework. The trajectory analysis process consolidates all self-reasoning trajectories from previous processes into a chain of reasoning snippets. By analyzing these trajectories internally, the LLM can output concise analyses and short answers. This step aims to enhance overall performance by providing both long-form and short-form answers based on the reasoning trajectories generated. Additionally, data generation and quality control play crucial roles in training RALMs. To generate training data for the relevance-aware process, GPT-4 is utilized to generate labels for irrelevant fields and reasons why certain documents cannot answer specific questions. Positive samples are created by concatenating questions with retrieved documents, while negative samples involve randomly selecting different questions with related documents shuffled to avoid order bias. Overall, this refined summary highlights how the proposed self-reasoning framework improves upon existing methods by enhancing reliability and traceability in RALMs without relying on external inference models or additional training components like critic or generator models.