Improving the reasoning capabilities of large language models (LLMs) typically requires supervised fine-tuning with labeled data or computationally expensive sampling. We introduce Unsupervised Prefix Fine-Tuning (UPFT), which leverages the observation of Prefix Self-Consistency -- the shared initial reasoning steps across diverse solution trajectories -- to enhance LLM reasoning efficiency. By training exclusively on the initial prefix substrings (as few as 8 tokens), UPFT removes the need for labeled data or exhaustive sampling. Experiments on reasoning benchmarks show that UPFT matches the performance of supervised methods such as Rejection Sampling Fine-Tuning, while reducing training time by 75% and sampling cost by 99%.
"The author of The Learner's Apprentice: AI and the Amplification of Human Creativity, Ken Kahn sits down with CMK Press President Sylvia Martinez to discuss his new book, learning, and artificial intelligence. In this video, Dr. Kahn shares some of the sorts of hundreds of projects explored in his best-selling new book."
Learn more about the new book: https://amzn.to/41cQgY0
Portkey AI Gateway allows application developers to easily integrate generative AI models, seamlessly switch among models, and add features like conditional routing without changing application code.
Yelp reviewed various large language models (LLMs) for correctness, relevance, and tone to enhance its AI assistant, improving user experience and engagement.
Claude Code is an agentic coding tool by Anthropic that operates in your terminal, understanding and modifying your codebase through natural language commands. It streamlines development workflows by executing commands, fixing bugs, and managing Git operations without requiring additional servers or complex setup.
A look at everything going on in the world of Replit, including the revamped mobile app, free checkpoints for Agent/Assistant, and the trend of 'vibe coding'.
This article introduces the pyramid search approach using Agentic Knowledge Distillation to address the limitations of traditional RAG strategies in document ingestion.
The pyramid structure allows for multi-level retrieval, including atomic insights, concepts, abstracts, and recollections. This structure mimics a knowledge graph but uses natural language, making it more efficient for LLMs to interact with.
Knowledge Distillation Process:
- Conversion to Markdown: Documents are converted to Markdown for better token efficiency and processing.
- Atomic Insights Extraction: Each page is processed using a two-page sliding window to generate a list of insights in simple sentences.
- Concept Distillation: Higher-level concepts are identified from the insights to reduce noise and preserve essential information.
- Abstract Creation: An LLM writes a comprehensive abstract for each document, capturing dense information efficiently.
- Recollections/Memories: Critical information useful across all tasks is stored at the top of the pyramid.
The attention mechanism in Large Language Models (LLMs) helps derive the meaning of a word from its context. This involves encoding words as multi-dimensional vectors, calculating query and key vectors, and using attention weights to adjust the embedding based on contextual relevance.
A terminal-based platform to experiment with the AI Software Engineer. It allows users to specify software in natural language, watch as an AI writes and executes the code, and implement improvements. Supports various models and customization options.
LangChain was once a promising framework for building AI applications powered by Large Language Models (LLMs). However, developers are now quitting LangChain due to issues like unnecessary complexity, unstable updates, and inconsistent documentation. The article explores the reasons behind this trend and offers insights into alternative solutions.
