10). Protein LLMs A comprehensive overview of Protein LLMs, including architectures, training datasets, evaluation metrics, and applications.
LLMS
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LightThinker: Dynamic Compression of LLM Reasoning Steps
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8). LightThinker This new paper proposes a novel approach to dynamically compress reasoning steps in LLMs, significantly improving efficiency without sacrificing accuracy.
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PlanGEN: Multi-Agent Framework Enhances LLM Planning
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6). PlanGEN PlanGEN is a multi-agent framework designed to enhance planning and reasoning in LLMs through constraint-guided iterative verification and adaptive algorithm selection.
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METAL Multi-Agent Framework Enhances Automatic Chart-to-Code Generation
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7). A Multi-Agent Framework for Chart Generation METAL is a vision-language model (VLM)-based multi-agent framework designed to significantly enhance automatic chart-to-code generation by decomposing the task into specialized iterative steps.
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Chain-of-Draft Reduces Latency in Reasoning Language Models
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3). Chain-of-Draft To address the issue of latency in reasoning LLMs, this work introduces Chain-of-Draft (CoD).
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OpenAI Introduces GPT-4.5 with Enhanced Safety and Alignment
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2). GPT-4.5 OpenAI introduces GPT-4.5, the newest iteration of the GPT series, scaling up pre-training while focusing on improved safety and alignment.
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Top AI Papers of the Week: GPT-4.5, Claude 3.7, and More
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Here are the top AI Papers of the Week (Feb 24 – Mar 2): – GPT-4.5
– PlanGEN
– Protein LLMs
– Chain-of-Draft
– Claude 3.7 Sonnet
– Emergent Misalignment Read on for more: -
Anthropic releases Claude 3.7 Sonnet with extended thinking mode
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1). Claude 3.7 Sonnet Anthropic releases a system card for its latest hybrid reasoning model, Claude 3.7 Sonnet, detailing safety measures, evaluations, and a new "extended thinking" mode.
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GPT-4.5 users demand continued support for specific model
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GPT-4.5 is the first time people have been emailing with such passion asking us to promise to never stop offering a specific model or even replace it with an update great work @kaicathyc @rapha_gl @mia_glaese
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Text Data Handling for Building Large Language Models From Scratch
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A new video in my “Build A Large Language Model From Scratch” series is now live. In this tutorial, I cover text data handling, which is an important component for both understanding and training LLMs.
In particular, the video walks through the following topics:
– The process of