Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2404.04302 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911828832944128 |
|---|---|
| author | Wiratunga, Nirmalie Abeyratne, Ramitha Jayawardena, Lasal Martin, Kyle Massie, Stewart Nkisi-Orji, Ikechukwu Weerasinghe, Ruvan Liret, Anne Fleisch, Bruno |
| author_facet | Wiratunga, Nirmalie Abeyratne, Ramitha Jayawardena, Lasal Martin, Kyle Massie, Stewart Nkisi-Orji, Ikechukwu Weerasinghe, Ruvan Liret, Anne Fleisch, Bruno |
| contents | Retrieval-Augmented Generation (RAG) enhances Large Language Model (LLM) output by providing prior knowledge as context to input. This is beneficial for knowledge-intensive and expert reliant tasks, including legal question-answering, which require evidence to validate generated text outputs. We highlight that Case-Based Reasoning (CBR) presents key opportunities to structure retrieval as part of the RAG process in an LLM. We introduce CBR-RAG, where CBR cycle's initial retrieval stage, its indexing vocabulary, and similarity knowledge containers are used to enhance LLM queries with contextually relevant cases. This integration augments the original LLM query, providing a richer prompt. We present an evaluation of CBR-RAG, and examine different representations (i.e. general and domain-specific embeddings) and methods of comparison (i.e. inter, intra and hybrid similarity) on the task of legal question-answering. Our results indicate that the context provided by CBR's case reuse enforces similarity between relevant components of the questions and the evidence base leading to significant improvements in the quality of generated answers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_04302 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CBR-RAG: Case-Based Reasoning for Retrieval Augmented Generation in LLMs for Legal Question Answering Wiratunga, Nirmalie Abeyratne, Ramitha Jayawardena, Lasal Martin, Kyle Massie, Stewart Nkisi-Orji, Ikechukwu Weerasinghe, Ruvan Liret, Anne Fleisch, Bruno Computation and Language Artificial Intelligence Retrieval-Augmented Generation (RAG) enhances Large Language Model (LLM) output by providing prior knowledge as context to input. This is beneficial for knowledge-intensive and expert reliant tasks, including legal question-answering, which require evidence to validate generated text outputs. We highlight that Case-Based Reasoning (CBR) presents key opportunities to structure retrieval as part of the RAG process in an LLM. We introduce CBR-RAG, where CBR cycle's initial retrieval stage, its indexing vocabulary, and similarity knowledge containers are used to enhance LLM queries with contextually relevant cases. This integration augments the original LLM query, providing a richer prompt. We present an evaluation of CBR-RAG, and examine different representations (i.e. general and domain-specific embeddings) and methods of comparison (i.e. inter, intra and hybrid similarity) on the task of legal question-answering. Our results indicate that the context provided by CBR's case reuse enforces similarity between relevant components of the questions and the evidence base leading to significant improvements in the quality of generated answers. |
| title | CBR-RAG: Case-Based Reasoning for Retrieval Augmented Generation in LLMs for Legal Question Answering |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2404.04302 |