You can try it yourself on your own setup by following the doc! https://
langchain-ai.github.io/langchain-benc
hmarks/notebooks/extraction/chat_extraction.html
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LLMS
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LangChain Chat Extraction Benchmarks Documentation Guide
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Testing LLM Performance with Prompting and Decoding Techniques
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We tested popular models like GPT-4, Claude, Llama, and Yi using different prompting and decoding techniques to see how much they impact performance for things like: – Question classification
– Sentiment analysis
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GPT-4 and Llama Performance Comparison on Tool-Calling Benchmark
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Unsurprisingly, GPT-4 with tool-calling ability performed the best on this benchmark, but Llama, even when not fine-tuned, performed well . See the comparison at this link: https://
smith.langchain.com/public/00f4444
c-9460-4a82-b87a-f50096f1cfef/d/compare?selectedSessions=0c022691-a7ac-4545-b2bc-58aab2d476e8d522dbfc-c09b-45a9-b11e-26aa95a3555a
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Prompt Engineering Strategies Impact on Llama Model Performance
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We also explored different prompting strategies using a 32B Llama model to see how they impact output performance. For this task (and model), simple, static strategies didn’t work that well in isolation!
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LangChain releases new LLM structured extraction benchmark
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Anyone building with LLMs knows structured extraction is key. Today we are releasing a new extraction benchmark testing LLM ability to infer structured data from chat logs. We've also shared some initial results below: https://
blog.langchain.dev/extraction-ben
chmarking/
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3 GPTs, including Master AI, boost ChatGPT with autonomous agents
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These 3 #GPTs take #ChatGPT to the next level → https:// youtu.be/yYj4Yz9tJRs Just the first one: Master AI It allows you to create a team of autonomous AI agents to carry out any action!
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5 Practical Insights for Building LLM-Based Businesses
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Good morning fellow AI enthusiast! This is the sixth video of my series for our free course "Training & Fine-Tuning LLMs for Production"! Here are 5 practical insights/tools for LLM-based businesses: https://
open.substack.com/pub/louisbouch
ard/p/5-steps-to-build-language-models?r=25qlky&utm_campaign=post&utm_medium=web
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European Disagreements on Foundation Models Regulation
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[#Article] The future of the #AIAct in suspense: divergences within the #EU on the #regulation of foundation models
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Condensing Token Embeddings into Single Vectors for Compute Efficiency
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oh yes I've been thinking about some similar things. so you want to condense sequences of token embeddings into a single vector to save compute. I think my research shows this should work in theory (you can condense lots of text into a vector without losing information)
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5 Essential Steps to Building Language Models Applications
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5 Essential Steps to Building Language Models Apps Dive into the world of Large Language Models (LLMs) with the essential steps to build, refine, and deploy AI-powered applications. Learn about selecting the right LLM, tailoring it to your needs, evaluating its performance,