Computer Science > Computation and Language
arXiv:2403.06840 (cs)
[Submitted on 11 Mar 2024 (v1), last revised 6 Jun 2024 (this version, v2)]
Title:RA-ISF: Learning to Answer and Understand from Retrieval Augmentation via Iterative Self-Feedback
View a PDF of the paper titled RA-ISF: Learning to Answer and Understand from Retrieval Augmentation via Iterative Self-Feedback, by Yanming Liu and 6 other authors
View PDFHTML (experimental)Abstract:Large language models (LLMs) demonstrate exceptional performance in numerous tasks but still heavily rely on knowledge stored in their parameters. Moreover, updating this knowledge incurs high training costs. Retrieval-augmented generation (RAG) methods address this issue by integrating external knowledge. The model can answer questions it couldn't previously by retrieving knowledge relevant to the query. This approach improves performance in certain scenarios for specific tasks. However, if irrelevant texts are retrieved, it may impair model performance. In this paper, we propose Retrieval Augmented Iterative Self-Feedback (RA-ISF), a framework that iteratively decomposes tasks and processes them in three submodules to enhance the model's problem-solving capabilities. Experiments show that our method outperforms existing benchmarks, performing well on models like GPT3.5, Llama2, significantly enhancing factual reasoning capabilities and reducing hallucinations.
| Comments: | 20 pages, multiple figures. Providing second version RA-ISF |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2403.06840 [cs.CL] |
| (orarXiv:2403.06840v2 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2403.06840 arXiv-issued DOI via DataCite |
Submission history
From: Yanming Liu [view email][v1] Mon, 11 Mar 2024 16:01:05 UTC (1,582 KB)
[v2] Thu, 6 Jun 2024 11:55:36 UTC (1,586 KB)
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View a PDF of the paper titled RA-ISF: Learning to Answer and Understand from Retrieval Augmentation via Iterative Self-Feedback, by Yanming Liu and 6 other authors
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