Podcasts — page 2.
RAFT: Adapting Language Model to Domain Specific RAG
Introduction Where adapting LLMs to specialized domains is essential (e.g., recent news, enterprise private documents), we discuss a…
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LLM Interpretability and Sparse Autoencoders: Research from OpenAI and Anthropic
Introduction It’s been an exciting couple weeks for GenAI! Join us as we discuss the latest research from…
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Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment
Introduction We break down a paper, Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment.…
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Breaking Down EvalGen: Who Validates the Validators?
Introduction Due to the cumbersome nature of human evaluation and limitations of code-based evaluation, Large Language Models (LLMs)…
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Anthropic Claude 3
Introduction In this week’s Arize Community Paper Reading we dive into the latest buzz in the AI world—the…
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Reinforcement Learning in the Era of LLMs
Introduction This week, we explore Reinforcement Learning in the Era of LLMs: What is Essential? What is needed?…
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Sora: OpenAI’s Text-to-Video Generation Model
Introduction This week, we talk about the implications of Text-to-Video Generation and speculate as to the possibilities (and…
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Sora: OpenAI’s Text-to-Video Generation Model
Introduction This week, we discuss the implications of Text-to-Video Generation and speculate as to the possibilities (and limitations)…
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RAG vs Fine-Tuning
Introduction This week we discussed “RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture.” This paper…
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Phi-2 Model
Introduction With only 2.7 billion parameters, Phi-2 surpasses the performance of Mistral and Llama-2 models at 7B and…
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Mistral AI (Mixtral-8x7B): Performance, Benchmarks
Introduction For the last paper read of the year, Arize CPO & Co-Founder, Aparna Dhinakaran, is joined by…
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How to Prompt LLMs for Text-to-SQL
Introduction For this paper read, we’re joined by Shuaichen Chang, now an Applied Scientist at AWS AI Lab…
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Arize gives AI teams observability and evals to understand and improve agent performance.
