Revolutionizing AI Dialogue Summarization: 80% Noise Reduction with Local SLMs

This article highlights a groundbreaking approach to improving the efficiency of summarizing AI dialogue logs. By strategically preprocessing the input data to remove noise, the author achieves a remarkable 80% reduction, significantly enhancing the quality of summaries generated by local **Large Language Model (LLM)**s. This innovative technique paves the way for more effective and efficient AI interactions.

• •The core innovation involves preprocessing AI dialogue logs to filter out irrelevant information like code blocks and tool outputs.
• •The preprocessing strategy focuses on identifying and removing noise, which accounts for a significant portion of the raw log data.
• •This approach enables more efficient and accurate summarization using local **LLM**s, such as Ollama with qwen2.5:14b.