@inproceedings{ma-etal-2024-context,
title = "Context-Driven Index Trimming: A Data Quality Perspective to Enhancing Precision of {RALM}s",
author = "Ma, Kexin and
Jin, Ruochun and
Haotian, Wang and
Xi, Wang and
Chen, Huan and
Tang, Yuhua and
Wang, Qian",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-emnlp.281",
pages = "4886--4901",
abstract = "Retrieval-Augmented Large Language Models(RALMs) have made significant strides in enhancing the accuracy of generated responses. However, existing research often overlooks the data quality issues within retrieval results, often caused by inaccurate existing vector-distance-based retrieval methods. We propose to boost the precision of RALMs{'} answers from a data quality perspective through the Context-Driven Index Trimming (CDIT) framework, where Context Matching Dependencies (CMDs) are employed as logical data quality rules to capture and regulate the consistency between retrieved contexts. Based on the semantic comprehension capabilities of Large Language Models (LLMs), CDIT can effectively identify and discard retrieval results that are inconsistent with the query context and further modify indexes in the database, thereby improving answer quality. Experiments demonstrate average improvement of 3.75{\%} in accuracy on challenging open-domain question-answering tasks. Also, the flexibility of CDIT is verified through its compatibility with various language models and indexing methods, which offers a promising approach to bolster RALMs{'} data quality and retrieval precision jointly.",
}
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%0 Conference Proceedings
%T Context-Driven Index Trimming: A Data Quality Perspective to Enhancing Precision of RALMs
%A Ma, Kexin
%A Jin, Ruochun
%A Haotian, Wang
%A Xi, Wang
%A Chen, Huan
%A Tang, Yuhua
%A Wang, Qian
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F ma-etal-2024-context
%X Retrieval-Augmented Large Language Models(RALMs) have made significant strides in enhancing the accuracy of generated responses. However, existing research often overlooks the data quality issues within retrieval results, often caused by inaccurate existing vector-distance-based retrieval methods. We propose to boost the precision of RALMs’ answers from a data quality perspective through the Context-Driven Index Trimming (CDIT) framework, where Context Matching Dependencies (CMDs) are employed as logical data quality rules to capture and regulate the consistency between retrieved contexts. Based on the semantic comprehension capabilities of Large Language Models (LLMs), CDIT can effectively identify and discard retrieval results that are inconsistent with the query context and further modify indexes in the database, thereby improving answer quality. Experiments demonstrate average improvement of 3.75% in accuracy on challenging open-domain question-answering tasks. Also, the flexibility of CDIT is verified through its compatibility with various language models and indexing methods, which offers a promising approach to bolster RALMs’ data quality and retrieval precision jointly.
%U https://aclanthology.org/2024.findings-emnlp.281
%P 4886-4901
Markdown (Informal)
[Context-Driven Index Trimming: A Data Quality Perspective to Enhancing Precision of RALMs](https://aclanthology.org/2024.findings-emnlp.281) (Ma et al., Findings 2024)
ACL
- Kexin Ma, Ruochun Jin, Wang Haotian, Wang Xi, Huan Chen, Yuhua Tang, and Qian Wang. 2024. Context-Driven Index Trimming: A Data Quality Perspective to Enhancing Precision of RALMs. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 4886–4901, Miami, Florida, USA. Association for Computational Linguistics.