Direct Preference Optimization: Your Language Model is Secretly a Reward Model Rafailov et al.: https://
arxiv.org/abs/2305.18290 #ArtificialIntelligence #DeepLearning #MachineLearning
LLMS
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Direct Preference Optimization: Language Models as Reward Models
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Direct Preference Optimization: Language Model as Reward Model
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Direct Preference Optimization: Your Language Model is Secretly a Reward Model Rafailov et al.: https://
arxiv.org/abs/2305.18290 #ArtificialIntelligence #DeepLearning #MachineLearning -
LeCun’s LLM Thoughts and Academic Attribution Concerns
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Sources: https://
en.wikipedia.org/wiki/Literatur
e_review
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and https://
ncbi.nlm.nih.gov/pmc/articles/P
MC2812776/
… The fact that LeCun’s recent thoughts on LLMs resemble my own earlier thoughts is widely known, and has been documented elsewhere. The fact he routinely he fails to recognize that work is evident in virtually all of -
Mixtral Offloading Repository Release and Documentation
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Full repo: https://
github.com/dvmazur/mixtra
l-offloading/tree/master?tab=readme-ov-file
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LLMs Show Interest in Learning COBOL Programming Language
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You know who’s interested in learning COBOL? LLMs
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Web Scraping Wikipedia Using LLM Agents Tutorial
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How to Web Scrape Wikipedia with LLM Agents We this tutorial by Kenneth Leung. It shows how to take a CSV of entities and then use an agent to populate information about those entities by looking things up on Wikipedia Many real-world use cases! https://
medium.datadriveninvestor.com/how-to-web-scr
ape-wikipedia-using-llm-agents-f0dba8400692
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LLMs and Humans Have Different Strengths and Weaknesses
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True. But to be more precise, they each have very different strengths and weaknesses. No way I could “read” as many books as an LLM does.
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Code Training Empowers LLMs: Generation, Reasoning, Agents
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9/ How Code Empowers LLMs – an overview of the benefits of training LLMs with code-specific data. Some capabilities include enhanced code generation, enabling reasoning, function calling, automated self-improvements, and serving intelligent agents.
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DocLLM: Visual Document Reasoning with Bounding Box Spatial Layout
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8/ DocLLM – an extension to traditional LLMs for reasoning over visual documents; focuses on using bounding box information to incorporate spatial layout structure; demonstrates SoTA on 14 of 16 datasets across several document intelligence tasks.
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LLM Augmented LLMs: Composing Models for Expanded Capabilities
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5/ LLM Augmented LLMs – explore composing existing foundation models with specific models to expand capabilities; introduce cross-attention between models to compose representations that enable new capabilities.