Questions about LLMs in Group Chats
The article explores the use of large language models in group chats, analyzing various frameworks like MUCA to improve conversational dynamics and bot agency through tunable features.
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The article discusses the exploration of using large language models (LLMs) in group chat environments, particularly within the xenocog community. The author expresses interest in how these models can interact naturally in group settings, raising questions about their operational mechanics, such as message visibility, response triggers, context management, and the ability to maintain ongoing conversations. Various platforms and frameworks are examined, including Shapes, which allows for tuning bot behaviors but lacks the desired conversational flow, and AutoGen, which focuses on task-oriented agents rather than true group interactions. The author also references the Generative Agents paper, which simulates human behavior in a town, suggesting that a similar memory system could be beneficial for group chats. The Multi-User Chat Assistant (MUCA) framework is highlighted for its focus on the complexities of multi-user interactions, emphasizing the need for content, timing, and recipient intelligence. The author aims to compile a list of tunable features from these methodologies to create a more effective framework for LLMs in group chats, ultimately seeking to enhance the conversational dynamics and agency of the bots involved.
- The author is exploring how LLMs can interact in group chats.
- Various frameworks and platforms for LLM interactions are analyzed.
- The Multi-User Chat Assistant (MUCA) framework addresses complex group chat dynamics.
- The author seeks to compile tunable features for improved LLM group interactions.
- The exploration aims to enhance conversational flow and bot agency in group settings.
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7 commentsthis is something that's talked a lot about in education -- how to foster a productive group discussion: https://www.teacher.org/blog/what-is-the-harkness-discussion...
Also, I have no idea what the use case for this would be but making it work sounds cool and kinda fruitful.
Related
Overcoming the Limits of Large Language Models
Large language models (LLMs) like chatbots face challenges such as hallucinations, lack of confidence estimates, and citations. MIT researchers suggest strategies like curated training data and diverse worldviews to enhance LLM performance.
Baiting the Bots
An experiment showed that simpler bots can maintain extended conversations with large language models, revealing implications for chatbot detection and potential denial-of-service risks due to LLMs' computational inefficiency.