Key Points
1. Retrieval-augmented language models (RALMs) have advanced the capabilities of large language models by integrating external knowledge sources, but suffer from issues such as unreliable retrieved information, fact-oriented hallucinations, and an inability to assess whether they possess adequate knowledge to provide an accurate answer.
2. A novel approach called C HAIN - OF -N OTING (C O N) is introduced to improve the robustness of RALMs by generating sequential reading notes for retrieved documents, enabling a thorough evaluation of their relevance to the given question and integrating this information to formulate the final answer.
3. Experiments show that RALMs equipped with C O N significantly outperform standard RALMs, achieving an average improvement in Exact Match (EM) score of +7.9 given entirely noisy retrieved documents, and a +10.5 improvement in rejection rates for real-time questions that fall outside the pre-training knowledge scope.
4. RALMs with C O N exhibit superior robustness in handling unknown scenarios, particularly evident in a special case evaluation called RealTimeQA, showing a significant improvement in their ability to reject questions in unknown scenarios.
5. The C O N framework presents a solution to the challenges faced by retrieval-augmented language models, effectively balancing direct information retrieval, contextual inference, and the acknowledgment of knowledge boundaries to produce more accurate and contextually relevant responses.
6. Case studies demonstrate that RALMs with C O N exhibit a deeper understanding of how documents reveal information relevant to the question, going beyond superficial terms to produce more accurate responses compared to standard RALMs.
7. Experimental results show that RALMs with C O N consistently outperform standard RALMs, particularly in scenarios with exclusively noisy documents, demonstrating enhanced noise robustness and the capability to ignore irrelevant information.
8. The model's training involves generating contextual reading notes for each retrieved document, synthesizing information to create a consolidated final response, incorporating a weighted loss strategy to ensure focus on accuracy and reliability of the final answer.
9. The study contributes a novel methodology designed to enhance the robustness of RALMs, effectively addressing their limitations and significantly improving performance in various open-domain question-answering benchmarks.
Summary
The research paper focuses on addressing the limitations of retrieval-augmented language models (RALMs). RALMs integrate large language models with external knowledge sources and are capable of reducing factual hallucinations by leveraging extensive evidence from external sources. However, the reliability of retrieved information is not always guaranteed, leading to misguided responses and potentially causing the model to overlook its inherent knowledge. The paper introduces a novel approach called C HAIN - OF -N OTE (C O N), which aims to improve the robustness of RALMs in facing noisy, irrelevant documents and in handling unknown scenarios.
C O N generates sequential reading notes for retrieved documents, enabling a thorough evaluation of their relevance to the given question, and integrates this information to formulate the final answer. The framework involves three distinct types of reading notes based on the relevance of retrieved documents to the input question.
The study conducted experiments across four open-domain QA benchmarks and demonstrated that RALMs equipped with C O N significantly outperformed standard RALMs. C O N achieved an average improvement of +7.9 in exact match score with noisy retrieved documents and a +10.5 increase in the rejection rates for real-time questions that fell outside the pre-training knowledge scope. Additionally, case studies showed that the C O N framework exhibits a deeper understanding of how documents reveal information relevant to the questions, leading to more accurate responses.
The paper also includes experimental settings and evaluations, and a discussion of the limitations of existing RALMs, highlighting the importance of addressing challenges such as surface-level processing, handling contradictory information, reduced transparency, and overdependence on retrieved documents. The robustness of C O N in handling noise and unknown scenarios was thoroughly evaluated to showcase its effectiveness in enhancing RALMs. The study validates the potential of C O N in improving the performance and reliability of RALMs in open-domain question answering tasks.
Reference: https://arxiv.org/abs/2311.09210