Improving Retrieval Augmented Language Model with Self-Reasoning
(2407.19813)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 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.
Overview
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The paper depicts an innovative self-reasoning framework to improve the reliability and traceability of Retrieval-Augmented Language Models (RALMs).
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Key processes in the framework include Relevance-Aware Process (RAP), Evidence-Aware Selective Process (EAP), and Trajectory Analysis Process (TAP), enhancing document relevance assessment, citation accuracy, and coherence of responses.
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Experimental results demonstrate the framework's superior performance in short-form and long-form QA, and fact verification tasks by effectively handling noisy retrieval data and generating accurate citations.
Improving Retrieval Augmented Language Model with Self-Reasoning
The paper "Improving Retrieval Augmented Language Model with Self-Reasoning" presents an innovative approach to enhance the reliability and traceability of Retrieval-Augmented Language Models (RALMs). The method introduces a self-reasoning framework designed to generate robust, accurate, and interpretable responses by embedding self-generated reasoning trajectories during both training and inference phases. The methodology addresses prevalent issues in RALMs, such as the detrimental effect of irrelevant document retrieval and the lack of explicit citations in generated outputs.
Core Contributions
The paper delineates three primary self-reasoning processes:
- Relevance-Aware Process (RAP): This process empowers the LLM to judge the relevance between retrieved documents and a given query. It outputs relevance assessments and justifications, improving document selection accuracy.
- Evidence-Aware Selective Process (EAP): Focused on identifying key sentences within relevant documents, this process requires the LLM to explicitly cite these sentences and explain their significance in answering the query.
- Trajectory Analysis Process (TAP): This consolidates the reasoning trajectories generated by RAP and EAP, enabling the LLM to provide a coherent analysis and deduce the final answer.
Methodology and Implementation
The framework was trained using a dataset of 2,000 high-quality examples generated by GPT-4. Quality control measures were employed to ensure data accuracy, such as using off-the-shelf tools to verify document citations and filtering out incorrect reasoning trajectories. The training methodology included a gradual learning approach, further refining the model's capability to generate long reasoning trajectories without error accumulation.
Experimental Evaluation
The framework was evaluated on four public datasets: NaturalQuestions (NQ), PopQA, ASQA (a long-form QA dataset), and FEVER (a fact verification dataset).
- Short-form QA: The framework demonstrated superior performance compared to existing methods, such as Self-RAG and fine-tuned LLaMA2 models, due to its ability to effectively handle and utilize noisy retrieval data.
- Long-form QA: Notable improvements were observed in metrics like EM recall and citation accuracy, highlighting the model's strength in multi-document comprehension and citation generation.
- Fact Verification: The proposed framework displayed a significantly higher accuracy rate than competing models on the FEVER dataset, attributed to the RAP's effectiveness in filtering irrelevant documents.
Detailed Analysis
An ablation study affirmed the individual contributions of RAP, EAP, and TAP. Excluding any of these processes led to notable declines in performance, underscoring their importance. Additionally, the robustness of the model was tested against noisy data and document order randomness, where the self-reasoning framework outperformed other models, demonstrating resilience against irrelevant information.
Citation Evaluation
Automated and human evaluations were conducted to assess the quality of document citations. Results indicated a strong correlation between human and automatic assessments, emphasizing the framework's capability to generate accurate and contextually appropriate citations.
Implications and Future Directions
The paper's contributions have several theoretical and practical implications:
- Theoretical Implications: The self-reasoning framework enhances the interpretability of LLM outputs, providing a structured approach to reasoning that can be scrutinized and verified. This approach could potentially influence the design of future LLMs, emphasizing the integration of self-reasoning capabilities.
- Practical Implications: The framework's robustness against noisy data and its effective citation mechanism make it highly valuable for knowledge-intensive applications, such as legal document analysis, scientific research, and fact-checking systems.
Conclusion
This paper presents a significant advancement in the domain of Retrieval-Augmented Language Models through an end-to-end self-reasoning framework. While focusing on open domain QA and fact verification tasks, the framework's versatility and robustness suggest its potential applicability to more complex reasoning tasks, such as arithmetic or multi-hop reasoning. Future work could explore these domains, further enhancing the model's generalization capabilities and mitigating inherent factual hallucinations in LLMs.
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