Tencent AI Lab Introduces Chain-of-Noting (CoN) to Improve Robustness and Reliability of Augmented Recall Language Models

Tencent ai Lab researchers address challenges in the reliability of augmented retrieval language models (RALMs), which can retrieve irrelevant information, leading to wrong answers. The proposed approach, CHAIN ​​OF NOTES (CON), aims to improve RALM. CON-equipped RALMs exhibit substantial performance improvements on open-domain QA benchmarks, achieving notable gains in exact match (EM) scores and rejection rates for out-of-scope questions.

The research addresses the limitations of RALMs, emphasizing robustness to noise and lower dependence on retrieved documents. The CON approach generates sequential reading notes for retrieved documents, allowing for a comprehensive assessment of relevance. The case studies highlight that CON improves the model’s understanding of document relevance, resulting in more accurate and contextually relevant responses by filtering out irrelevant or less reliable content.

Outperforming standard RALMs, CON achieves exact match scores and higher rejection rates for out-of-scope questions. It balances direct retrieval, inferential reasoning, and recognition of knowledge gaps, resembling human information processing. The implementation of CON involves the design of lecture notes, data collection and model training, offering a solution to the current limitations of RALM and improving reliability.

CON, a framework that generates sequential read notes for retrieved documents, improves the performance of RALMs. Trained on a LLaMa-2 7B model with training data created by ChatGPT, CON outperforms standard RALMs, especially in high noise scenarios. It classifies reading notes into direct answers, useful context, and unknown scenarios, demonstrating a robust mechanism for evaluating document relevance. Comparisons with LLaMa-2 wo IR, a reference method, show CON’s ability to filter out irrelevant content, improving response accuracy and contextual relevance.

RALMs equipped with CON demonstrate substantial improvements, achieving a notable average increase of +7.9 in EM score for fully noisy recovered documents. CON shows a notable +10.5 improvement in rejection rates for real-time questions beyond pre-training knowledge. Evaluation metrics include EM score, F1 score, and rejection rate for open domain QA. The case studies highlight the effectiveness of CON in deepening the understanding of RALMs, addressing the challenges of noisy and irrelevant documents, and improving overall robustness.

The CON framework significantly improves RALMs. By generating sequential reading notes for the retrieved documents and integrating this information into the final response, CON-equipped RALMs outperform standard RALMs, showing a notable average improvement. CON addresses the limitations of standard RALMs, fostering a deeper understanding of relevant information and improving overall performance on several open-domain QA benchmarks.

Future research can expand the application of the CON framework to various domains and tasks, evaluating its generalizability and effectiveness in strengthening RALMs. Investigating various retrieval strategies and document classification methods can optimize the retrieval process and improve the relevance of recovered documents. User studies should evaluate the usability and satisfaction of RALMs with CON in real-world scenarios, considering the quality and reliability of the response. Exploring additional external knowledge sources and combining CON with techniques such as pre-training or tuning can further improve the performance and adaptability of RALM.

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