Original Paper: https://arxiv.org/abs/2407.19813
By: Yuan Xia, Jingbo Zhou, Zhenhui Shi, Jun Chen, Haifeng Huang
Abstract:
The Retrieval-Augmented Language Model (RALM) has shown remarkable performance on knowledge-intensive tasks by incorporating external knowledge during inference, which mitigates the factual hallucinations inherited in large language models (LLMs). Despite these advancements, challenges persist in the implementation of RALMs, particularly concerning their reliability and traceability. To be specific, the irrelevant document retrieval may result in unhelpful response generation or even deteriorate the performance of LLMs, while the lack of proper citations in generated outputs complicates efforts to verify the trustworthiness of the models. To this end, we propose a novel self-reasoning framework aimed at improving the reliability and traceability of RALMs, whose core idea is to leverage reasoning trajectories generated by the LLM itself. The framework involves constructing self-reason trajectories with three processes: a relevance-aware process, an evidence-aware selective process, and a trajectory analysis process. We have evaluated our framework across four public datasets (two short-form QA datasets, one long-form QA dataset, and one fact verification dataset) to demonstrate the superiority of our method, which can outperform existing state-of-art models and can achieve comparable performance with GPT-4, while only using 2,000 training samples.
In the landscape of natural language processing, the Retrieval-Augmented Language Model (RALM) is a game-changer for knowledge-intensive tasks. By incorporating external knowledge during inference, RALMs significantly reduce the factual inaccuracies often seen in large language models (LLMs). However, RALMs are not without their challenges. Issues related to reliability, traceability, and the retrieval of irrelevant documents can hamper their performance. Enter the revolutionary self-reasoning framework designed to address these limitations.
The self-reasoning framework enhances RALMs by generating reasoning trajectories using the LLM itself. This is achieved through three critical processes:
These processes collectively ensure that the RALM can not only retrieve and use relevant information but also provide a transparent reasoning path that makes it easier to verify the outputs.