ConvMix: A Mixed-Criteria Data Augmentation Framework for Conversational Dense Retrieval

• URL: http://arxiv.org/abs/2508.04001v1
• Date: Wed, 06 Aug 2025 01:28:49 GMT
• Title: ConvMix: A Mixed-Criteria Data Augmentation Framework for Conversational Dense Retrieval
• Authors: Fengran Mo, Jinghan Zhang, Yuchen Hui, Jia Ao Sun, Zhichao Xu, Zhan Su, Jian-Yun Nie,
• Abstract summary: We propose ConvMix, a mixed-criteria framework to augment conversational dense retrieval.<n>We design a two-sided relevance judgment augmentation schema in a scalable manner via the aid of large language models.<n> Experimental results on five widely used benchmarks show that the conversational dense retriever trained by our ConvMix framework outperforms previous baseline methods.
• Score: 25.129468117978767
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Conversational search aims to satisfy users' complex information needs via multiple-turn interactions. The key challenge lies in revealing real users' search intent from the context-dependent queries. Previous studies achieve conversational search by fine-tuning a conversational dense retriever with relevance judgments between pairs of context-dependent queries and documents. However, this training paradigm encounters data scarcity issues. To this end, we propose ConvMix, a mixed-criteria framework to augment conversational dense retrieval, which covers more aspects than existing data augmentation frameworks. We design a two-sided relevance judgment augmentation schema in a scalable manner via the aid of large language models. Besides, we integrate the framework with quality control mechanisms to obtain semantically diverse samples and near-distribution supervisions to combine various annotated data. Experimental results on five widely used benchmarks show that the conversational dense retriever trained by our ConvMix framework outperforms previous baseline methods, which demonstrates our superior effectiveness.

Related papers

• Reinforcing Compositional Retrieval: Retrieving Step-by-Step for Composing Informative Contexts [67.67746334493302]
  Large Language Models (LLMs) have demonstrated remarkable capabilities across numerous tasks, yet they often rely on external context to handle complex tasks.<n>We propose a tri-encoder sequential retriever that models this process as a Markov Decision Process (MDP)<n>We show that our method consistently and significantly outperforms baselines, underscoring the importance of explicitly modeling inter-example dependencies.
  arXiv  Detail & Related papers  (2025-04-15T17:35:56Z)
• Leveraging Inter-Chunk Interactions for Enhanced Retrieval in Large Language Model-Based Question Answering [12.60063463163226]
  IIER captures the internal connections between document chunks by considering three types of interactions: structural, keyword, and semantic. It identifies multiple seed nodes based on the target question and iteratively searches for relevant chunks to gather supporting evidence. It refines the context and reasoning chain, aiding the large language model in reasoning and answer generation.
  arXiv  Detail & Related papers  (2024-08-06T02:39:55Z)
• Improving Retrieval in Sponsored Search by Leveraging Query Context Signals [6.152499434499752]
  We propose an approach to enhance query understanding by augmenting queries with rich contextual signals. We use web search titles and snippets to ground queries in real-world information and utilize GPT-4 to generate query rewrites and explanations. Our context-aware approach substantially outperforms context-free models.
  arXiv  Detail & Related papers  (2024-07-19T14:28:53Z)
• Query-oriented Data Augmentation for Session Search [71.84678750612754]
  We propose query-oriented data augmentation to enrich search logs and empower the modeling. We generate supplemental training pairs by altering the most important part of a search context. We develop several strategies to alter the current query, resulting in new training data with varying degrees of difficulty.
  arXiv  Detail & Related papers  (2024-07-04T08:08:33Z)
• CART: A Generative Cross-Modal Retrieval Framework with Coarse-To-Fine Semantic Modeling [53.97609687516371]
  Cross-modal retrieval aims to search for instances, which are semantically related to the query through the interaction of different modal data.<n>Traditional solutions utilize a single-tower or dual-tower framework to explicitly compute the score between queries and candidates.<n>We propose a generative cross-modal retrieval framework (CART) based on coarse-to-fine semantic modeling.
  arXiv  Detail & Related papers  (2024-06-25T12:47:04Z)
• ConvSDG: Session Data Generation for Conversational Search [29.211860955861244]
  We propose a framework to explore the feasibility of boosting conversational search by using large language models (LLMs) for session data generation. Within this framework, we design dialogue/session-level and query-level data generation with unsupervised and semi-supervised learning. The generated data are used to fine-tune the conversational dense retriever.
  arXiv  Detail & Related papers  (2024-03-17T20:34:40Z)
• Effective and Efficient Conversation Retrieval for Dialogue State Tracking with Implicit Text Summaries [48.243879779374836]
  Few-shot dialogue state tracking (DST) with Large Language Models (LLM) relies on an effective and efficient conversation retriever to find similar in-context examples for prompt learning. Previous works use raw dialogue context as search keys and queries, and a retriever is fine-tuned with annotated dialogues to achieve superior performance. We handle the task of conversation retrieval based on text summaries of the conversations. A LLM-based conversation summarizer is adopted for query and key generation, which enables effective maximum inner product search.
  arXiv  Detail & Related papers  (2024-02-20T14:31:17Z)
• CONVERSER: Few-Shot Conversational Dense Retrieval with Synthetic Data Generation [32.10366004426449]
  We propose CONVERSER, a framework for training conversational dense retrievers with at most 6 examples of in-domain dialogues. We utilize the in-context learning capability of large language models to generate conversational queries given a passage in the retrieval corpus. Experimental results on conversational retrieval benchmarks OR-QuAC and TREC CAsT 19 show that the proposed CONVERSER achieves comparable performance to fully-supervised models.
  arXiv  Detail & Related papers  (2023-09-13T06:40:24Z)
• SSP: Self-Supervised Post-training for Conversational Search [63.28684982954115]
  We propose fullmodel (model) which is a new post-training paradigm with three self-supervised tasks to efficiently initialize the conversational search model. To verify the effectiveness of our proposed method, we apply the conversational encoder post-trained by model on the conversational search task using two benchmark datasets: CAsT-19 and CAsT-20.
  arXiv  Detail & Related papers  (2023-07-02T13:36:36Z)
• Query Expansion Using Contextual Clue Sampling with Language Models [69.51976926838232]
  We propose a combination of an effective filtering strategy and fusion of the retrieved documents based on the generation probability of each context. Our lexical matching based approach achieves a similar top-5/top-20 retrieval accuracy and higher top-100 accuracy compared with the well-established dense retrieval model DPR. For end-to-end QA, the reader model also benefits from our method and achieves the highest Exact-Match score against several competitive baselines.
  arXiv  Detail & Related papers  (2022-10-13T15:18:04Z)

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.