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TREC iKAT 2023: The Interactive Knowledge Assistance Track Overview

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arxiv 2401.01330 v2 pith:E7RLU3O4 submitted 2024-01-02 cs.IR cs.AIcs.CL

Mohammad Aliannejadi , Zahra Abbasiantaeb , Shubham Chatterjee , Jeffery Dalton , Leif Azzopardi This is my paper · ORCID
classification cs.IRcs.AIcs.CL
keywords conversationalikatuseragentscontextinformationlanguagelarge
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Conversational Information Seeking has evolved rapidly in the last few years with the development of Large Language Models providing the basis for interpreting and responding in a naturalistic manner to user requests. iKAT emphasizes the creation and research of conversational search agents that adapt responses based on the user's prior interactions and present context. This means that the same question might yield varied answers, contingent on the user's profile and preferences. The challenge lies in enabling Conversational Search Agents (CSA) to incorporate personalized context to effectively guide users through the relevant information to them. iKAT's first year attracted seven teams and a total of 24 runs. Most of the runs leveraged Large Language Models (LLMs) in their pipelines, with a few focusing on a generate-then-retrieve approach.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Adaptive Personalized Conversational Information Retrieval

    cs.IR 2025-08 conditional novelty 6.0 of 10

    Explicit per-turn personalization level detection plus per-level weighted fusion of personalized and non-personalized query rewrites improves retrieval on TREC iKAT 2023 and 2024.

  2. RALI@TREC iKAT 2024: Achieving Personalization via Retrieval Fusion in Conversational Search

    cs.IR 2024-12 conditional novelty 5.0 of 10

    Fusing BM25 rankings from non-personalized, expanded, and personalized query rewrites achieved the best passage retrieval scores for RALI at TREC iKAT 2024, though no ablation isolates the fusion effect.

  3. Deep Research Agents: A Systematic Examination And Roadmap

    cs.AI 2025-06 conditional novelty 4.0 of 10

    A survey that organizes LLM-powered deep research agents into static versus dynamic workflows and single versus multi agent architectures, and reviews their benchmarks and open challenges.

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