CIR at iKAT SCAI 2026: Exploring Clarification Need Prediction in Agentic Conversational Search

2026-07-22

AI Summary

This work proposes an end-to-end conversational search agent that integrates query rewriting, retrieval reranking, answer generation, and a clarification mechanism to enhance both interaction efficiency and retrieval quality. The key innovation lies in embedding a neural clarification module—comprising clarification need prediction and clarifying question generation—directly into the conversational search pipeline, enabling proactive clarification of user intent. Experimental results demonstrate that the proposed approach significantly improves overall system performance and validate the effectiveness of various clarification prediction models in real-world conversational scenarios.

Abstract

This paper presents the participation of the Cologne Information Retrieval group in the iKAT SCAI 2026 shared task. We use an agentic conversational search system, equipped with tools for query rewriting, retrieval and reranking, answer generation, and clarification need prediction and clarification question generation. We experiment with two different neural clarification need prediction models.

Problem

• clarification need prediction
• conversational search
• agentic search
• information retrieval
• question generation

Innovation

• agentic conversational search
• clarification need prediction
• neural prediction models
• clarification question generation
• query rewriting