Into the Unknown Unknowns: Engaged Human Learning through Participation in Language Model Agent Conversations (2408.15232v2)

Abstract: While LLM (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.

• The paper introduces Collaborative STORM, enabling multiparty LLM agent collaboration to bridge the gap in exploring unknown unknowns in information seeking.
• The study demonstrates how role-differentiated agents and a dynamic mind map structure lead to higher novelty, breadth, and depth in generated reports.
• Through automatic and human evaluations, the system shows improved user engagement and reduced cognitive effort compared to traditional search methods.

Collaborative STORM: Facilitating Knowledge Discovery through Multiparty Discourse Among LLM Agents

The paper "Into the Unknown Unknowns: Engaged Human Learning through Participation in LLM Agent Conversations" explores the capabilities of LLMs to assist users in complex information-seeking tasks. This paper introduces Collaborative STORM (\system), a novel system that deviates from traditional search and QA paradigms by fostering an environment where users can observe and occasionally participate in collaborative discourses among multiple LLM agents with distinct roles.

Key Innovations and Methodology

Bridging the Gap in Complex Information Seeking

Traditional IR models and even advanced generative search engines can efficiently address "known unknowns" by providing direct responses to specific queries. However, they fall short when it comes to scenarios requiring the discovery of "unknown unknowns"—topics or questions users might not even know they need to ask. \system addresses this gap by emulating educational settings where knowledge is explored through dynamic, multiparty conversations.

Collaborative Discourse and Role Differentiation

\system simulates conversations involving three roles: topic-specific experts, a moderator, and the user. The experts provide diverse perspectives by posing questions, requesting information, or proposing potential answers based on retrieved data. The moderator steers the discourse towards novel and unexplored areas, ensuring the conversation remains productive and aligned with the user's broader goals. This division of roles effectively mitigates echo chamber effects and cognitive overload that can be prevalent in single-agent interaction systems.

Dynamic Mind Map for Information Organization

A standout feature in \system is its use of a dynamic, hierarchical mind map to organize and track the evolving discourse. This tool helps users tirelessly follow and contribute to the conversation without losing context, ultimately summarizing the discovered information in a comprehensive report. The mind map is updated via "insert" and "reorganize" operations, ensuring a coherent structure that accurately reflects the breadth and depth of the conversation.

Automatic Evaluation Metrics and Results

The research introduces the WildSeek dataset, a collection of real information-seeking records that serve as the basis for evaluating \system. Using both automatic and human evaluation metrics, \system demonstrated notable superiority over baseline systems (RAG chatbots and traditional search engines) in terms of report quality and user engagement.

Key metrics include:

• Breadth and Depth: \system outperformed baselines by generating reports that were rated higher in breadth (covering a wide array of relevant subtopics) and depth (providing detailed explorations of those subtopics).
• Novelty and Serendipity: \system enabled the discovery of new, unexpected information, as evidenced by higher novelty scores.
• Engagement and Mental Effort: Feedback from human evaluations underscored \system’s ability to maintain user engagement and reduce mental effort through well-organized discourse and intuitive mind mapping.

Implications and Future Directions

The implications of \system for both theoretical and practical applications are multifaceted. Practically, it can revolutionize how academic research, market analysis, and multifaceted decision-making tasks are approached by providing a more interactive, user-friendly experience that accommodates the dynamic nature of complex information needs. Theoretically, \system exemplifies advancements in multi-agent collaboration, shedding light on the potential of LLMs to function cooperatively in facilitating human learning.

Future developments could explore enhancing the personalization of the system to better adapt to a user's knowledge level and evolving needs. Additionally, expanding \system to support multilingual capabilities and optimizing response generation times could further increase its utility and accessibility.

Conclusion

By fostering a collaborative environment where humans can learn through multiparty conversations among LLM agents, \system represents a significant stride in AI-assisted information seeking. This research underscores the potential for more interactive and engaging human-AI interfaces, paving the way for innovative approaches to knowledge discovery and learning.