Learning Contextual Retrieval for Robust Conversational Search (EMNLP 2025)

Forum topic · 小凯 · 2026-07-05

Summary

This EMNLP 2025 main conference paper, Learning Contextual Retrieval for Robust Conversational Search, addresses how retrieval models can remain effective in multi-turn conversational search where user intent is context-dependent and queries are often underspecified. The work sits at the intersection of information retrieval, retrieval-augmented generation (RAG), and LLM-based conversational systems, targeting challenges such as coreference resolution, context modeling across turns, robustness to query reformulation, and the gap between static passage retrieval and dynamic dialogue-driven information needs. The accompanying forum analysis situates the paper within the broader evolution from BM25 and dense bi-encoders to cross-encoder rerankers, generative retrieval, and agentic search pipelines, discussing typical architectural components (retriever, reranker, planner, generator, feedback mechanisms) and learning strategies such as contrastive learning, distillation, and reinforcement learning. Evaluation in this space commonly uses benchmarks like MS MARCO, BEIR, and Natural Questions with metrics such as nDCG@10, MRR, and Recall@k, alongside latency, cost, and answer trustworthiness considerations for production deployment. Open problems include evaluation reliability, hallucination and safety, cross-lingual generalization, and test-time compute allocation. Full quantitative results should be verified against the original PDF at the ACL Anthology link.

Learning Contextual Retrieval for Robust Conversational Search (EMNLP 2025, ACL)

Paper: Learning Contextual Retrieval for Robust Conversational Search
Venue: EMNLP 2025 Main Conference (ACL Anthology)
Link: https://aclanthology.org/2025.emnlp-main.602/
Topic area: Conversational Search

Key points

The paper targets contextual retrieval for conversational search, where queries in a dialogue are context-dependent, ambiguous, and often underspecified, making conventional single-query retrieval brittle.
It addresses the core LLM-era question of how to redistribute responsibilities among retrieval, reranking, generation, and tool calling within an end-to-end conversational search stack.

Context and motivation

Large-scale search, recommendation, and personalization systems increasingly rely on conversational interfaces. Traditional pipelines treat retrieval, ranking, and generation as separate stages, which struggles to meet modern demands for natural-language interaction, multi-hop reasoning, and up-to-date knowledge. This work is positioned at the intersection of conversational search and large-scale IR, covering open-domain information access, enterprise knowledge retrieval, and architectures that couple external knowledge sources with generative models.

Typical method pipeline

Following the common "formalization → design → training → inference" pattern described in the forum analysis:

1. Input & representation: encode queries, documents, and user dialogue context into dense/sparse representations or structured prompts. 2. Core modules: retriever, reranker, planner, memory modules, and tool interfaces, connected in series or parallel. 3. Learning strategies: supervised fine-tuning, contrastive learning, distillation, reinforcement learning (including process rewards), and bootstrapped data synthesis. 4. Inference strategies: single-shot retrieval, iterative retrieval, parallel sub-queries, early stopping, and budget control.

Evaluation

Typical evaluation protocols in this space (verify exact numbers in the original PDF):

Datasets: MS MARCO, BEIR, Natural Questions, domain 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.
Ablations: contribution of retrieval steps, reranking depth, and training data scale.

Insights for search / recommendation / personalization

1. Architecture: cascaded retrieval + rerank + generation remains dominant, but agentic paradigms are making the *retrieval policy itself* learnable. 2. Data: high-quality instruction data and click/session logs matter; synthetic data must guard against leakage and distribution shift. 3. Evaluation: the gap between offline metrics and online satisfaction is widening; LLM-as-judge needs cross-validation with human assessment. 4. Production: latency, cost, explainability, and safety are hard constraints that cannot be traded away for benchmark gains.

Limitations and open problems

Potential limitations include experiment scale bounded by compute budgets, benchmark–user distribution mismatch, English-centric data limiting cross-lingual generalization, and safety risks of agentic systems on the open web. Future directions include more efficient test-time compute allocation, deeper integration with knowledge graphs and structured databases, and causal/fairness constraints for recommendation.

Related entries

A Survey of Conversational Search, Sep 2025, ACM
Engineering Conversational Search Systems: A Review of Applications
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
CoSearchAgent: A Lightweight Collaborative Search Agent with Large Language Models

Glossary

| Term | Meaning | |------|---------| | IR | Information Retrieval | | RAG | Retrieval-Augmented Generation | | LTR | Learning to Rank | | nDCG | Normalized Discounted Cumulative Gain | | Agentic Search | Search modeled as sequential decision-making and tool use | | Gen-IR | Generative Information Retrieval |