Podcasts — page 3.
The Geometry of Truth: Emergent Linear Structure in LLM Representation of True/False Datasets
Introduction For this paper read, we’re joined by Samuel Marks, Postdoctoral Research Associate at Northeastern University, to discuss…
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Towards Monosemanticity: Decomposing Language Models With Dictionary Learning
Introduction In this paper read, we discuss “Towards Monosemanticity: Decomposing Language Models With Dictionary Learning,” a paper from…
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RankVicuna: Zero-Shot Listwise Document Reranking with Open-Source Large Language Models
Introduction In this paper reading, we’ll be discussing RankVicuna, the first fully open-source LLM capable of performing high-quality…
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Explaining Grokking Through Circuit Efficiency
Introduction Join Arize Co-Founder & CEO Jason Lopatecki, and ML Solutions Engineer, Sally-Ann DeLucia, as they discuss “Explaining…
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Large Content And Behavior Models to Understand, Simulate, and Optimize Content and Behavior.
Introduction Amber Roberts and Sally-Ann DeLucia discuss “Large Content And Behavior Models To Understand, Simulate, And Optimize Content…
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Skeleton of Thought: LLMs Can Do Parallel Decoding Paper Reading
Introduction Join us for an exploration of the ‘Skeleton-of-Thought’ (SoT) approach, aimed at reducing large language model latency…
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Extending the Context Window of LLaMA Models Paper Reading
Introduction During this week’s paper reading event, we are thrilled to announce that we will be joined by…
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Llama 2: Open Foundation and Fine-Tuned Chat Models Paper Reading
Introduction In this paper reading, we explore the paper “Llama 2: Open Foundation and Fine-Tuned Chat Models.” The…
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Lost in the Middle: How LLMs Use Long Contexts
Introduction This paper examines how well language models utilize longer input contexts. The study focuses on multi-document question…
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Orca: Progressive Learning from Complex Explanation Traces of GPT-4 Paper Reading
Introduction Recent research focuses on improving smaller models through imitation learning using outputs from large foundation models (LFMs).…
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One-for-All: Generalized LoRA for Parameter-Efficient Fine-tuning
Introduction In this week’s paper reading, we discuss “One-for-All: Generalized LoRA for Parameter-Efficient Fine-tuning.” GLoRA is a universal,…
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HyDE: Precise Zero-Shot Dense Retrieval without Relevance Labels
Introduction In this paper reading, we explore HyDE: Precise Zero-Shot Dense Retrieval without Relevance Labels. HyDE is a…
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