mlsanityData to 7 Oct 2026

The 3rd Place Solution of CCIR CUP 2025: A Framework for Retrieval-Augmented Generation in Multi-Turn Legal Conversation

arXiv 2510.15722 ↗First posted 17 Oct 2025cs.IRcited by 0

On the map: Retrieval-augmented generation → Legal and financial RAG

Abstract (arXiv)

Retrieval-Augmented Generation has made significant progress in the field of natural language processing. By combining the advantages of information retrieval and large language models, RAG can generate relevant and contextually appropriate responses based on items retrieved from reliable sources. This technology has demonstrated outstanding performance across multiple domains, but its application in the legal field remains in its exploratory phase. In this paper, we introduce our approach for "Legal Knowledge Retrieval and Generation" in CCIR CUP 2025, which leverages large language models and information retrieval systems to provide responses based on laws in response to user questions.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Legal and financial RAG, one of 15 directions in Retrieval-augmented generation. Builds on 6 of its 22 references that are in the map. First posted on arXiv 17 Oct 2025.

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6 of 22 references.

Closest ancestors · 3

Same direction, newest first

  1. 2024HyPA-RAG: A Hybrid Parameter Adaptive Retrieval-Augmented Generation System for AI Legal and Policy Applications29 Aug
  2. 2024LegalBench-RAG: A Benchmark for Retrieval-Augmented Generation in the Legal Domain19 Aug
  3. 2024CBR-RAG: Case-Based Reasoning for Retrieval Augmented Generation in LLMs for Legal Question Answering4 Apr

Other references · 3

  1. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  2. 2024RAG-QA Arena: Evaluating Domain Robustness for Long-form Retrieval Augmented Question Answeringcited by 43
  3. 2024DR-RAG: Applying Dynamic Document Relevance to Retrieval-Augmented Generation for Question-Answeringcited by 14

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  1. 2025LexRAG: Benchmarking Retrieval-Augmented Generation in Multi-Turn Legal Consultation Conversation
  2. 2026Fine-grained Claim-level RAG Benchmark for Law
  3. 2025All for law and law for all: Adaptive RAG Pipeline for Legal Research
  4. 2025LegalRAG: A Hybrid RAG System for Multilingual Legal Information Retrieval
  5. 2026Legal-DC: Benchmarking Retrieval-Augmented Generation for Legal Documents
  6. 2025A Reasoning-Focused Legal Retrieval Benchmark
  7. 2026NepKANUN: A RAG-Based Nepali Legal Assistant
  8. 2026Retrieval Augmented Generation Framework for the Nepali Legal Domain Question Answering