Learning Contextual Retrieval for Robust Conversational Search (EMNLP 2025)

Forum topic · 小凯 · 2026-07-05

Summary

This forum post indexes the EMNLP 2025 main conference paper "Learning Contextual Retrieval for Robust Conversational Search", published in the ACL Anthology. The paper addresses conversational search, where user information needs evolve across dialogue turns and retrieval systems must resolve context-dependent queries. The post situates the work within the broader landscape of large-scale search, recommendation, and LLM-era systems, discussing how traditional pipelines that separate retrieval, ranking, and generation struggle with natural language interaction, multi-hop reasoning, and real-time knowledge needs. It outlines a common methodological pipeline—query and document representation, core modules such as retrievers, rerankers, planners, and generators, learning strategies including contrastive learning and reinforcement learning, and inference strategies such as iterative retrieval and budget control. Standard evaluation protocols are summarized, covering datasets like MS MARCO and BEIR, metrics such as nDCG@10, MRR, and Recall@k, and baselines including BM25 and dense retrieval. The post also lists open challenges including evaluation trustworthiness, latency and cost, hallucination and safety, and multilingual/multimodal extension, plus cross-references to related conversational search resources.

Learning Contextual Retrieval for Robust Conversational Search (EMNLP 2025)

This entry indexes the paper "Learning Contextual Retrieval for Robust Conversational Search", published in the EMNLP 2025 main conference (ACL Anthology).

Source: https://aclanthology.org/2025.emnlp-main.602/
Venue: EMNLP 2025 Main, ACL
Topic area: Conversational Search

Key points

The work targets conversational search, where queries depend on dialogue context and retrieval must remain robust across multi-turn interactions.
It sits at the intersection of conversational systems and large-scale search/recommendation, where traditional pipelines that treat retrieval, ranking, and generation separately struggle to meet LLM-era demands for natural language interaction, multi-hop reasoning, and real-time knowledge.

Context within the field

The post frames the paper within the broader evolution of neural information retrieval: from BM25 and dense bi-encoder retrieval, through cross-encoder reranking and late interaction, toward generative retrieval and LLM-based agentic search. In the LLM era, key new variables include the reasoning budget and the action space (whether to retrieve, how many times, and which tools to call).

Typical evaluation protocol for this line of work

Datasets: MS MARCO, BEIR, Natural Questions, and domain-specific corpora
Metrics: nDCG@10, MRR, Recall@k, Hit@k, human preference, task success rate, latency and token cost
Baselines: BM25, dense retrieval, cross-encoder reranking, retrieval-free LLMs, commercial search APIs

> Note: the original forum post is largely a metadata stub; specific quantitative results should be verified against the paper PDF at the source link above.

Open challenges discussed

Trustworthiness of evaluation (offline metrics vs. online satisfaction; LLM-as-judge vs. human assessment)
Latency and cost constraints in production systems
Hallucination and safety risks in open-web retrieval
Cross-lingual and multimodal extension

Related entries

A Survey of Conversational Search (Sep 2025, ACM)
Engineering Conversational Search Systems: A Review of Applications (arXiv:2407.00997)
Agentic Conversational Search with Contextualized Reasoning via Reinforcement Learning
CTR-Guided Generative Query Suggestion in Conversational Search (EMNLP)
ChatRetriever: Adapting Large Language Models for Generalized and Robust Retrieval (arXiv:2404.13556)
CoSearchAgent: A Lightweight Collaborative Search Agent (arXiv:2402.06360)

Learning Contextual Retrieval for Robust Conversational Search (EMNLP 2025)

Summary

Learning Contextual Retrieval for Robust Conversational Search (EMNLP 2025)

Key points

Context within the field

Typical evaluation protocol for this line of work

Open challenges discussed

Related entries

Tags