Exploring the Design Space of Real-time LLM Knowledge Support Systems: A Case Study of Jargon Explanations

Research Background and Problem

• Identified Problems or Challenges:
  Real-time knowledge support systems face significant challenges, including knowledge gaps due to diverse user backgrounds, users' lack of awareness of their own knowledge deficiencies, and barriers caused by the use of technical jargon during communication (i.e., "terminological redundancy"). Additionally, traditional knowledge support systems often fail to provide real-time assistance, making them unable to meet user needs at critical moments.

• Why This Problem Is Important:
  Knowledge gaps can lead to misunderstandings or even collaboration failures, particularly in cross-disciplinary communication. For instance, studies have shown that frequent use of terminological redundancy within organizations hinders effective team collaboration. Such barriers not only affect daily operations but may also limit future cross-disciplinary innovation or collaboration.

• Research Motivation and Related Work:
  The authors were inspired by knowledge management systems and large language model (LLM) technologies. Traditional systems rely on predefined data repositories and cannot address unanticipated knowledge gaps in real time. Modern LLMs have the potential to quickly generate real-time knowledge support content, but further research is needed on designing knowledge representation formats, reducing users' cognitive load, and improving the relevance and credibility of the information provided.

Solution

• Proposed Method or Solution:
  The authors developed a prototype system called "StopGap" to detect jargon in videos in real time and provide LLM-generated knowledge support, including four knowledge representation formats (definitions, analogies, lists, and images). The system also conducted a design probe study to explore the effectiveness of various visual knowledge representations and mapped out the design space for real-time knowledge support systems.

• Innovative Aspects of the Solution:

  • Utilizes LLMs to generate real-time knowledge support content without relying on predefined data repositories.
  • Combines knowledge visualization research to identify the practical utility of four core knowledge representation formats in real-time settings.
  • Proposes a design space to guide the development of future real-time knowledge support systems.

• Implementation Steps and Key Technologies:

  • Used Whisper for video audio-to-text transcription and jargon detection.
  • Leveraged GPT-4 to generate explanations and supportive content for detected terms.
  • Designed a front-end user interface to provide real-time knowledge support while synchronizing with users' video-watching experience.
  • Conducted experimental studies to collect quantitative (e.g., surveys) and qualitative (e.g., interviews) data to analyze user preferences and cognitive load.

Research Outcomes

• Specific Achievements:

  • The StopGap system was found to be highly useful by users for understanding jargon and content, with its automation significantly reducing the time spent on manual searches.
  • Demonstrated the strengths and weaknesses of different knowledge representation formats in real-time scenarios: definitions are suitable for quick access to direct information, analogies aid long-term memory, lists provide detailed information but may increase cognitive load, and images support rapid understanding but are less effective when semantics are ambiguous.
  • Identified users' strong demand for personalization, user agency, and hybrid proactive suggestions.

• Advantages Over Existing Solutions:

  • The StopGap system surpasses traditional knowledge support systems with its automation and real-time generation capabilities, offering greater flexibility in response.
  • The system's design emphasizes user experience, enabling more efficient understanding of jargon and reducing knowledge barriers.

• Experimental or Evaluation Results:

  • StopGap enhanced video content comprehension without significantly increasing cognitive load (based on NASA-TLX data), demonstrating the feasibility of the system for real-time knowledge support.
  • Interviews confirmed users' preferences for personalized knowledge representations and more complex interaction methods.

• Limitations and Future Directions:

  • Limitations include a small sample size, a focus on term explanations in knowledge support design, and insufficient evaluation of the credibility of LLM-generated content.
  • Future directions include: developing collaborative knowledge support systems; exploring more complex knowledge types (e.g., procedural and event knowledge); studying verification mechanisms for LLM-generated content to improve accuracy; and investigating the balance between user agency and automation.

Through this research, the authors not only proposed an effective prototype system but also provided a theoretical framework and practical foundation for the future development of intelligent knowledge support systems. This work contributes to addressing gaps in the field of real-time knowledge support and fosters innovation in cross-disciplinary communication tools.