Graph of Records: Boosting Retrieval Augmented Generation for Long-context Summarization with Graphs
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866910973468606464 |
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| author | Zhang, Haozhen Feng, Tao You, Jiaxuan |
| author_facet | Zhang, Haozhen Feng, Tao You, Jiaxuan |
| contents | Retrieval-augmented generation (RAG) has revitalized Large Language Models (LLMs) by injecting non-parametric factual knowledge. Compared with long-context LLMs, RAG is considered an effective summarization tool in a more concise and lightweight manner, which can interact with LLMs multiple times using diverse queries to get comprehensive responses. However, the LLM-generated historical responses, which contain potentially insightful information, are largely neglected and discarded by existing approaches, leading to suboptimal results. In this paper, we propose $\textit{graph of records}$ ($\textbf{GoR}$), which leverages historical responses generated by LLMs to enhance RAG for long-context global summarization. Inspired by the $\textit{retrieve-then-generate}$ paradigm of RAG, we construct a graph by establishing an edge between the retrieved text chunks and the corresponding LLM-generated response. To further uncover the intricate correlations between them, GoR features a $\textit{graph neural network}$ and an elaborately designed $\textit{BERTScore}$-based objective for self-supervised model training, enabling seamless supervision signal backpropagation between reference summaries and node embeddings. We comprehensively compare GoR with 12 baselines across four long-context summarization datasets, and the results indicate that our proposed method reaches the best performance ($\textit{e.g.}$, 15%, 8%, and 19% improvement over retrievers w.r.t. Rouge-L, Rouge-1, and Rouge-2 on the WCEP dataset). Extensive experiments further demonstrate the effectiveness of GoR. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_11001 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Graph of Records: Boosting Retrieval Augmented Generation for Long-context Summarization with Graphs Zhang, Haozhen Feng, Tao You, Jiaxuan Computation and Language Artificial Intelligence Machine Learning Retrieval-augmented generation (RAG) has revitalized Large Language Models (LLMs) by injecting non-parametric factual knowledge. Compared with long-context LLMs, RAG is considered an effective summarization tool in a more concise and lightweight manner, which can interact with LLMs multiple times using diverse queries to get comprehensive responses. However, the LLM-generated historical responses, which contain potentially insightful information, are largely neglected and discarded by existing approaches, leading to suboptimal results. In this paper, we propose $\textit{graph of records}$ ($\textbf{GoR}$), which leverages historical responses generated by LLMs to enhance RAG for long-context global summarization. Inspired by the $\textit{retrieve-then-generate}$ paradigm of RAG, we construct a graph by establishing an edge between the retrieved text chunks and the corresponding LLM-generated response. To further uncover the intricate correlations between them, GoR features a $\textit{graph neural network}$ and an elaborately designed $\textit{BERTScore}$-based objective for self-supervised model training, enabling seamless supervision signal backpropagation between reference summaries and node embeddings. We comprehensively compare GoR with 12 baselines across four long-context summarization datasets, and the results indicate that our proposed method reaches the best performance ($\textit{e.g.}$, 15%, 8%, and 19% improvement over retrievers w.r.t. Rouge-L, Rouge-1, and Rouge-2 on the WCEP dataset). Extensive experiments further demonstrate the effectiveness of GoR. |
| title | Graph of Records: Boosting Retrieval Augmented Generation for Long-context Summarization with Graphs |
| topic | Computation and Language Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2410.11001 |