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Distilling from Dialogues: Finding Meaning in LLM Interactions
- In the age of Large Language Models (LLMs), conversations with AI are common but challenging to track insights, leading to the need for personalized conversation summarization tools.
- The Adaptive/Progressive Summarization project focuses on personalized summaries tailored to individual user interactions, ensuring accuracy and relevance.
- Progressive summarization enhances existing summaries based on each dialogue, maintaining consistency and coherence.
- The project features prompt engineering to guide LLMs in generating accurate summaries, emphasizing the importance of effective prompt design.
- Collecting 'reasoning traces' involved utilizing the Google Gemini Flash Thinking API for generating traces and solutions for questions.
- The s1-32B model is fine-tuned to understand the 'Wait' token, enabling it to continue reasoning when it would typically stop.
- Limitations of the work include performance plateau, context window constraints, limited extrapolation, and dependence on pre-trained models.
- A Gradio-based application allows tracking and comparing summary changes over time, facilitating storage of learned information.
- The project was developed during Google's Vertex sprints, benefiting from GCP credits, and emphasized continued conversation and engagement.
- Distilling from Dialogues: Finding Meaning in LLM Interactions addresses the challenges of LLM interactions and the importance of insightful summarization.
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