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

• URL: http://arxiv.org/abs/2510.15722v1
• Date: Fri, 17 Oct 2025 15:12:15 GMT
• Title: The 3rd Place Solution of CCIR CUP 2025: A Framework for Retrieval-Augmented Generation in Multi-Turn Legal Conversation
• Authors: Da Li, Zecheng Fang, Qiang Yan, Wei Huang, Xuanpu Luo,
• Abstract summary: We introduce our approach for "Legal Knowledge Retrieval and Generation" in CCIR CUP 2025.<n>By combining the advantages of information retrieval and large language models, RAG can generate relevant and contextually appropriate responses.
• Score: 7.363804447752668
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• All for law and law for all: Adaptive RAG Pipeline for Legal Research [0.8819595592190884]
  Retrieval-Augmented Generation (RAG) has transformed how we approach text generation tasks.<n>This work introduces a novel end-to-end RAG pipeline that improves upon previous baselines.
  arXiv  Detail & Related papers  (2025-08-18T17:14:03Z)
• LegalRAG: A Hybrid RAG System for Multilingual Legal Information Retrieval [7.059964549363294]
  We develop an efficient bilingual question-answering framework for regulatory documents, specifically the Bangladesh Police Gazettes.<n>Our approach employs modern Retrieval Augmented Generation (RAG) pipelines to enhance information retrieval and response generation.<n>This system enables efficient searching for specific government legal notices, making legal information more accessible.
  arXiv  Detail & Related papers  (2025-04-19T06:09:54Z)
• Retrieval Augmented Generation and Understanding in Vision: A Survey and New Outlook [85.43403500874889]
  Retrieval-augmented generation (RAG) has emerged as a pivotal technique in artificial intelligence (AI)<n>Recent advancements in RAG for embodied AI, with a particular focus on applications in planning, task execution, multimodal perception, interaction, and specialized domains.
  arXiv  Detail & Related papers  (2025-03-23T10:33:28Z)
• Exploiting LLMs' Reasoning Capability to Infer Implicit Concepts in Legal Information Retrieval [6.952344923975001]
  This work focuses on utilizing the logical reasoning capabilities of large language models (LLMs) to identify relevant legal terms. The proposed retrieval system integrates additional information from the term--based expansion and query reformulation to improve the retrieval accuracy. Experiments on COLIEE 2022 and COLIEE 2023 datasets show that extra knowledge from LLMs helps to improve the retrieval result of both lexical and semantic ranking models.
  arXiv  Detail & Related papers  (2024-10-16T01:34:14Z)
• CodeRAG-Bench: Can Retrieval Augment Code Generation? [78.37076502395699]
  We conduct a systematic, large-scale analysis of code generation using retrieval-augmented generation.<n>We first curate a comprehensive evaluation benchmark, CodeRAG-Bench, encompassing three categories of code generation tasks.<n>We examine top-performing models on CodeRAG-Bench by providing contexts retrieved from one or multiple sources.
  arXiv  Detail & Related papers  (2024-06-20T16:59:52Z)
• DuetRAG: Collaborative Retrieval-Augmented Generation [57.440772556318926]
  Collaborative Retrieval-Augmented Generation framework, DuetRAG, proposed. bootstrapping philosophy is to simultaneously integrate the domain fintuning and RAG models.
  arXiv  Detail & Related papers  (2024-05-12T09:48:28Z)
• A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models [71.25225058845324]
  Large Language Models (LLMs) have demonstrated revolutionary abilities in language understanding and generation. Retrieval-Augmented Generation (RAG) can offer reliable and up-to-date external knowledge. RA-LLMs have emerged to harness external and authoritative knowledge bases, rather than relying on the model's internal knowledge.
  arXiv  Detail & Related papers  (2024-05-10T02:48:45Z)
• A Survey on Retrieval-Augmented Text Generation for Large Language Models [1.4579344926652844]
  Retrieval-Augmented Generation (RAG) merges retrieval methods with deep learning advancements. This paper organizes the RAG paradigm into four categories: pre-retrieval, retrieval, post-retrieval, and generation. It outlines RAG's evolution and discusses the field's progression through the analysis of significant studies.
  arXiv  Detail & Related papers  (2024-04-17T01:27:42Z)
• NeCo@ALQAC 2023: Legal Domain Knowledge Acquisition for Low-Resource Languages through Data Enrichment [2.441072488254427]
  This paper presents NeCo Team's solutions to the Vietnamese text processing tasks provided in the Automated Legal Question Answering Competition 2023 (ALQAC 2023) Our methods for the legal document retrieval task employ a combination of similarity ranking and deep learning models, while for the second task, we propose a range of adaptive techniques to handle different question types. Our approaches achieve outstanding results on both tasks of the competition, demonstrating the potential benefits and effectiveness of question answering systems in the legal field.
  arXiv  Detail & Related papers  (2023-09-11T14:43:45Z)
• Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy [164.83371924650294]
  We show that strong performance can be achieved by a method we call Iter-RetGen, which synergizes retrieval and generation in an iterative manner. A model output shows what might be needed to finish a task, and thus provides an informative context for retrieving more relevant knowledge. Iter-RetGen processes all retrieved knowledge as a whole and largely preserves the flexibility in generation without structural constraints.
  arXiv  Detail & Related papers  (2023-05-24T16:17:36Z)
• Multi-Stage Conversational Passage Retrieval: An Approach to Fusing Term Importance Estimation and Neural Query Rewriting [56.268862325167575]
  We tackle conversational passage retrieval (ConvPR) with query reformulation integrated into a multi-stage ad-hoc IR system. We propose two conversational query reformulation (CQR) methods: (1) term importance estimation and (2) neural query rewriting. For the former, we expand conversational queries using important terms extracted from the conversational context with frequency-based signals. For the latter, we reformulate conversational queries into natural, standalone, human-understandable queries with a pretrained sequence-tosequence model.
  arXiv  Detail & Related papers  (2020-05-05T14:30:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.