mlsanityData to 7 Oct 2026

Focus Agent: LLM-Powered Virtual Focus Group

arXiv 2409.01907 ↗First posted 3 Sep 2024cs.HCcited by 4

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

Abstract (arXiv)

In the domain of Human-Computer Interaction, focus groups represent a widely utilised yet resource-intensive methodology, often demanding the expertise of skilled moderators and meticulous preparatory efforts. This study introduces the ``Focus Agent,'' a Large Language Model (LLM) powered framework that simulates both the focus group (for data collection) and acts as a moderator in a focus group setting with human participants. To assess the data quality derived from the Focus Agent, we ran five focus group sessions with a total of 23 human participants as well as deploying the Focus Agent to simulate these discussions with AI participants. Quantitative analysis indicates that Focus Agent can generate opinions similar to those of human participants. Furthermore, the research exposes some improvements associated with LLMs acting as moderators in focus group discussions that include human participants.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 21 of its 61 references that are in the map. First posted on arXiv 3 Sep 2024.

Builds on

21 of 61 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023Multi-Agent Collaboration: Harnessing the Power of Intelligent LLM Agents5 Jun

Other references · 20

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
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  3. 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,500
  4. 2023Tree of Thoughts: Deliberate Problem Solving with Large Language Modelscited by 3,155
  5. 2020ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verificationcited by 606
  6. 2021Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigmcited by 599
  7. 2019CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Datacited by 544
  8. 2023Large Language Models for Information Retrieval: A Surveycited by 331
  9. 2024Hallucination is Inevitable: An Innate Limitation of Large Language Modelscited by 329
  10. 2023WhisperX: Time-Accurate Speech Transcription of Long-Form Audiocited by 320
  11. 2023Exploring Large Language Models for Communication Games: An Empirical Study on Werewolfcited by 238
  12. 2023Can large language models provide useful feedback on research papers? A large-scale empirical analysiscited by 195
8 more
  1. 2023Simulating Opinion Dynamics with Networks of LLM-based Agentscited by 153
  2. 2024Systematic Biases in LLM Simulations of Debatescited by 98
  3. 2020End-to-End Speaker Diarization for an Unknown Number of Speakers with Encoder-Decoder Based Attractorscited by 70
  4. 2023Let the LLMs Talk: Simulating Human-to-Human Conversational QA via Zero-Shot LLM-to-LLM Interactionscited by 70
  5. 2020Target-Speaker Voice Activity Detection: a Novel Approach for Multi-Speaker Diarization in a Dinner Party Scenariocited by 67
  6. 2016Achieving Human Parity in Conversational Speech Recognitioncited by 63
  7. 2023Large Language Models as Tax Attorneys: A Case Study in Legal Capabilities Emergencecited by 42
  8. 2020End-to-End Speaker Diarization as Post-Processingcited by 13

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 1.

4 papers.

  1. 2025Utility Engineering: Analyzing and Controlling Emergent Value Systems in AIscited by 63
  2. 2025AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classificationcited by 2
  3. 2025Artificially intelligent agents in the social and behavioral sciences: A history and outlookcited by 0
  4. 2026More Is Not More: What Matters for Diversity in LLM Opinions?cited by 0

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