# TREC iKAT 2023: The Interactive Knowledge Assistance Track Overview

arXiv: https://arxiv.org/abs/2401.01330 · HTML: https://mlsanity.com/paper/2401.01330/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 15 references that are in the map.
- First posted on arXiv 2 Jan 2024.

## Built on by (most cited first, 15 of 15)

- [Deep Research Agents: A Systematic Examination And Roadmap](https://mlsanity.com/paper/2506.18096/) (2025)
- [A Survey of Large Language Model Empowered Agents for Recommendation and Search: Towards Next-Generation Information Retrieval](https://mlsanity.com/paper/2503.05659/) (2025)
- [Ragnar"ok: A Reusable RAG Framework and Baselines for TREC 2024 Retrieval-Augmented Generation Track](https://mlsanity.com/paper/2406.16828/) (2024)
- [A Survey of Conversational Search](https://mlsanity.com/paper/2410.15576/) (2024)
- [TREC iKAT 2023: A Test Collection for Evaluating Conversational and Interactive Knowledge Assistants](https://mlsanity.com/paper/2405.02637/) (2024)
- [Can We Use Large Language Models to Fill Relevance Judgment Holes?](https://mlsanity.com/paper/2405.05600/) (2024)
- [How to Leverage Personal Textual Knowledge for Personalized Conversational Information Retrieval](https://mlsanity.com/paper/2407.16192/) (2024)
- [Synthetic Data Generation with Large Language Models for Personalized Community Question Answering](https://mlsanity.com/paper/2410.22182/) (2024)
- [Conversational Gold: Evaluating Personalized Conversational Search System using Gold Nuggets](https://mlsanity.com/paper/2503.09902/) (2025)
- [Adaptive Personalized Conversational Information Retrieval](https://mlsanity.com/paper/2508.08634/) (2025)
- [A Picture of Agentic Search](https://mlsanity.com/paper/2602.17518/) (2026)
- [Passage Query Methods for Retrieval and Reranking in Conversational Agents](https://mlsanity.com/paper/2503.00238/) (2025)
- [Second SIGIR Workshop on Simulations for Information Access (Sim4IA 2025)](https://mlsanity.com/paper/2505.11687/) (2025)
- [Dagstuhl Perspectives Workshop 24352 -- Conversational Agents: A Framework for Evaluation (CAFE): Manifesto](https://mlsanity.com/paper/2506.11112/) (2025)
- [CFDA & CLIP at TREC iKAT 2025: Enhancing Personalized Conversational Search via Query Reformulation and Rank Fusion](https://mlsanity.com/paper/2509.15588/) (2025)

## Abstract (arXiv)

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.