Détail du document
Identifiant
oai:arXiv.org:2408.15232
Sujet
Computer Science - Computation and... Computer Science - Artificial Inte... Computer Science - Information Ret... I.2.7 H.5.2 H.3.3Auteur
Jiang, Yucheng Shao, Yijia Ma, Dekun Semnani, Sina J. Lam, Monica S.Catégorie
Computer Science
Année
2024
Date de référencement
23/10/2024
Mots clés
language discourse unknown unknowns users user co-stormRésumé
While language model (LM)-powered chatbots and generative search engines excel at answering concrete queries, discovering information in the terrain of unknown unknowns remains challenging for users.
To emulate the common educational scenario where children/students learn by listening to and participating in conversations of their parents/teachers, we create Collaborative STORM (Co-STORM).
Unlike QA systems that require users to ask all the questions, Co-STORM lets users observe and occasionally steer the discourse among several LM agents.
The agents ask questions on the user's behalf, allowing the user to discover unknown unknowns serendipitously.
To facilitate user interaction, Co-STORM assists users in tracking the discourse by organizing the uncovered information into a dynamic mind map, ultimately generating a comprehensive report as takeaways.
For automatic evaluation, we construct the WildSeek dataset by collecting real information-seeking records with user goals.
Co-STORM outperforms baseline methods on both discourse trace and report quality.
In a further human evaluation, 70% of participants prefer Co-STORM over a search engine, and 78% favor it over a RAG chatbot.
;Comment: EMNLP 2024 Main
Jiang, Yucheng,Shao, Yijia,Ma, Dekun,Semnani, Sina J.,Lam, Monica S., 2024, Into the Unknown Unknowns: Engaged Human Learning through Participation in Language Model Agent Conversations
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