Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Models (2311.09210v2)

Abstract: Retrieval-augmented LLMs (RALMs) represent a substantial advancement in the capabilities of LLMs, notably in reducing factual hallucination by leveraging external knowledge sources. However, the reliability of the retrieved information is not always guaranteed. The retrieval of irrelevant data can lead to misguided responses, and potentially causing the model to overlook its inherent knowledge, even when it possesses adequate information to address the query. Moreover, standard RALMs often struggle to assess whether they possess adequate knowledge, both intrinsic and retrieved, to provide an accurate answer. In situations where knowledge is lacking, these systems should ideally respond with "unknown" when the answer is unattainable. In response to these challenges, we introduces Chain-of-Noting (CoN), a novel approach aimed at improving the robustness of RALMs in facing noisy, irrelevant documents and in handling unknown scenarios. The core idea of CoN is to generate 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. We employed ChatGPT to create training data for CoN, which was subsequently trained on an LLaMa-2 7B model. Our experiments across four open-domain QA benchmarks show that RALMs equipped with CoN significantly outperform standard RALMs. Notably, CoN achieves an average improvement of +7.9 in EM score given entirely noisy retrieved documents and +10.5 in rejection rates for real-time questions that fall outside the pre-training knowledge scope.

Enhancing the Robustness of Retrieval-Augmented LLMs with Chain-of-Note Framework

This paper introduces the "Chain-of-Note" (CoN) framework, a novel methodology that aims to enhance the robustness of retrieval-augmented LLMs (RALMs). The primary objective of the paper is to address the issue of processing irrelevant or noisy information that may be retrieved alongside accurate data during the query resolution process. The authors propose a structured note-taking process that enables a more thorough assessment of the relevance and reliability of the information in the retrieved documents.

Contributions and Methodology

The authors propose that RALMs, though advanced in leveraging external knowledge to mitigate knowledge gaps, often struggle with "noise," induced by irrelevant or conflicting retrieved data, and "unknown" robustness, which involves acknowledging when a reliable answer cannot be constructed from either retrieved or inherent knowledge. These shortcomings may lead to hallucinations or erroneous responses.

To address these limitations, the Chain-of-Note (CoN) framework is introduced. This framework involves the generation of reading notes for retrieved documents to systematically evaluate the relevance and credibility of the information before formulating responses to queries. A critical function of CoN is its ability to refine RALMs' responses by identifying and using the most pertinent and reliable information. The process effectively filters out irrelevant or less credible content and enhances the robustness of RALMs under conditions where documents are either noisy or outside the domain of pre-trained knowledge.

The CoN framework was tested by integrating it into a LLaMa-2 7B model, followed by training on a dataset generated by prompting ChatGPT. The effectiveness of CoN was evaluated across multiple open-domain QA datasets, including NQ, TriviaQA, WebQ, and RealTimeQA.

Results and Observations

The empirical evaluation reflects CoN's ability to significantly outperform the standard RALM systems. The results demonstrate:

• An improvement of +7.9 in EM score under scenarios where only irrelevant documents are retrieved.
• Enhanced unknown robustness, with a reported +10.5 in rejection rates for questions that extend beyond the training knowledge scope.

These results underscore CoN's efficacy in navigating the presence of noise in retrieved data and making informed decisions about when to acknowledge the limits of available knowledge by responding with "unknown."

Implications and Future Prospects

The developments proposed in this paper have strong implications for the fields of natural language processing and machine learning, particularly in improving the reliability of information-retrieving models. RALMs equipped with CoN can potentially result in more accurate and dependable deployments in real-world applications, such as complex question answering systems and AI customer services, where accuracy is paramount.

Looking ahead, the integration of the Chain-of-Note framework opens avenues for research focused on other dimensions of robustness in LLMs, such as dealing with different types of noise or enhancing multi-lingual and multi-domain adaptability. Furthermore, the CoN strategy illustrates the potential of augmenting retrieval processes with human-like reasoning capabilities, thus bridging a gap between pure information retrieval and contextual understanding.

In conclusion, this paper offers valuable insights and solutions to challenges that are prevalent in retrieval-augmented LLMs, bringing forth an innovative approach that underscores the importance of systematic data evaluation in enhancing model robustness and reliability.