Improving Retrieval Augmented Language Model with Self-Reasoning
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.
Summary Notes
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.
Understanding the Core: What is Self-Reasoning?
The self-reasoning framework enhances RALMs by generating reasoning trajectories using the LLM itself. This is achieved through three critical processes:
- Relevance-Aware Process (RAP)
- Evidence-Aware Selective Process (EAP)
- Trajectory Analysis Process (TAP)
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.
Key Methodologies in Self-Reasoning
Relevance-Aware Process (RAP)
In RAP, the LLM is instructed to judge the relevance of retrieved documents relative to a given question.
This step is crucial for filtering out irrelevant documents that could misguide the model.
The LLM also provides reasons for why a document is deemed relevant, ensuring a transparent decision-making process.
Evidence-Aware Selective Process (EAP)
Once relevant documents are identified, EAP directs the LLM to select key sentences from these documents that can serve as evidence.
These sentences are then cited, and the LLM provides reasons for why these citations are valid.
This selective process hones in on the most pertinent information, improving the model's ability to generate accurate responses.
Trajectory Analysis Process (TAP)
TAP consolidates the self-reasoning trajectories generated in the previous steps. The LLM analyzes these trajectories to produce a concise final answer.
This process not only ensures that the generated answers are well-supported by evidence but also enhances the interpretability and traceability of the model's outputs.
Main Findings and Results
The self-reasoning framework was evaluated across four public datasets: two short-form QA datasets (NaturalQuestions and PopQA), one long-form QA dataset (ASQA), and one fact verification dataset (FEVER). The results were impressive:
- The self-reasoning framework outperformed existing state-of-the-art models and achieved comparable performance to GPT-4, all while using only 2,000 training samples.
- On the NaturalQuestions dataset, the self-reasoning model achieved an accuracy of 41.4% compared to 38.8% by the best existing model, Self-RAG.
- In fact verification tasks using the FEVER dataset, the model reached an accuracy of 83.9%, significantly higher than other models like Vicuna (60.6%) and Self-RAG (72.1%).
Implications and Applications
The implications of these findings are substantial. The self-reasoning framework enhances the robustness of RALMs, making them more reliable for real-world applications where the accuracy of information is critical. Here are some potential applications:
- Medical Diagnosis: A self-reasoning RALM can provide more accurate medical information by cross-referencing multiple reliable sources, thereby aiding healthcare professionals.
- Legal Research: Lawyers can benefit from a model that not only retrieves relevant legal documents but also provides a transparent reasoning path, ensuring the reliability of the information.
- Academic Research: Researchers can leverage this technology to gather and verify information from various academic papers, ensuring the accuracy and reliability of their citations.
Conclusion
The self-reasoning framework represents a significant leap forward in the field of natural language processing.
By addressing the key challenges of reliability and traceability in RALMs, this framework ensures that LLMs can provide accurate, well-supported answers.
This not only enhances the effectiveness of knowledge-intensive tasks but also opens up new avenues for practical applications in various fields.
As we move forward, the focus will be on exploring more challenging scenarios and further refining the self-reasoning framework to handle tasks like multi-hop reasoning and code generation.
The future looks promising, with self-reasoning poised to become a cornerstone in the development of more reliable and interpretable language models.
