Learning Contextual Retrieval for Robust Conversational Search (EMNLP 2025)

This post introduces the EMNLP 2025 main-conference paper "Learning Contextual Retrieval for Robust Conversational Search", available in the ACL Anthology: https://aclanthology.org/2025.emnlp-main.602/.

Overview

The paper targets conversational search in the LLM era. Traditional pipeline approaches that treat retrieval, ranking, and generation as disconnected stages struggle to meet users' combined demands for natural language interaction, multi-hop reasoning, and up-to-date knowledge. This work proposes a learned contextual retrieval approach aimed at making conversational search more robust, and positions itself within the broader landscape of open-domain information access, enterprise knowledge retrieval, and retrieval-augmented generation.

Key points

• Problem area: Conversational search and personalization at scale, where efficiency, scalability, and user-intent understanding remain core challenges.
• System framing: The post decomposes modern search systems into components — representation learning, retrievers, re-rankers, planners, generators, and feedback mechanisms — a useful lens for engineering adoption.
• Learning strategies commonly relevant to this line of work: supervised fine-tuning, contrastive learning, distillation, reinforcement learning (including process rewards), and bootstrapped data synthesis.
• Inference strategies: single-pass retrieval, iterative retrieval, parallel sub-queries, early stopping, and compute/budget control.
• Typical evaluation setup in this field:
• 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 re-ranking, retrieval-free LLMs, commercial search APIs

Insights for search, recommendation, and personalization

1. Architecture: Cascaded retrieve–re-rank–generate remains mainstream, but agentic paradigms are making retrieval *policy* (whether and how often to retrieve) itself a learnable object. 2. Data: High-quality instruction data and click/session logs are as important as model design; synthetic data requires care against leakage and distribution shift. 3. Evaluation: The gap between offline metrics and online satisfaction is widening; LLM-as-judge should be cross-validated with human assessment. 4. Deployment: Latency, cost, explainability, and safety are hard production constraints that academic benchmarks alone do not capture.

Open problems

• Trustworthy evaluation protocols
• Latency and cost budgets in production
• Hallucination and safety in open-web retrieval
• Cross-lingual and multimodal generalization

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 LLMs for Generalized and Robust Retrieval (arXiv:2404.13556)
• CoSearchAgent: A Lightweight Collaborative Search Agent (arXiv:2402.06360)

> Note: This post is an index entry from a curated research list. Detailed quantitative results are not reproduced here; consult the original PDF at the ACL Anthology link above before citing specific numbers.