6/ LLM4Decompile A family of open-access decompilation LLMs ranging from 1B to 33B parameters; these models are trained on 4 billion tokens of C source code and corresponding assembly code.
@dair_ai
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RankPrompt: Self-Ranking Method for Better LLM Reasoning
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5/ Step-by-Step Comparisons Make LLMs Better Reasoners Proposes RankPrompt, a prompting method to enable LLMs to self-rank their responses without additional resources.
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Tool Use in Large Language Models: Overview and Applications
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4/ Tool Use in LLMs Provides an overview of tool use in LLMs, including a formal definition of the tool-use paradigm, scenarios where LLMs leverage tool usage, and for which tasks this approach works well.
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TacticAI: AI-Powered Football Tactics Assistant
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3/ TacticAI
— DAIR.AI (@dair_ai) 24 mars 2024
An AI-powered assistant for football tactics that offer coaches a way to sample and explore alternative player setups for a corner kick routine and select the tactic with the highest predicted likelihood of success.https://t.co/F7oi9edMSM3/ TacticAI An AI-powered assistant for football tactics that offer coaches a way to sample and explore alternative player setups for a corner kick routine and select the tactic with the highest predicted likelihood of success.
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Evolutionary Model Merge: Automating Foundation Model Development
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2/ Evolutionary Model Merge
— DAIR.AI (@dair_ai) 24 mars 2024
An approach for automating foundation model development using evolution to combine open-source models; facilitates cross-domain merging where a Japanese Math LLM achieved SOTA performance on Japanese LLM benchmarks.https://t.co/0pFqLGg0EY2/ Evolutionary Model Merge An approach for automating foundation model development using evolution to combine open-source models; facilitates cross-domain merging where a Japanese Math LLM achieved SOTA performance on Japanese LLM benchmarks.
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Design2Code: Multimodal LLMs automate front-end engineering
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9/ Design2Code Investigates the use of multimodal LLMs for converting a visual design into code implementation which is key for automating front-end engineering.
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TripoSR: Fast 3D Mesh Generation from Single Images
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10/ TripoSR
— DAIR.AI (@dair_ai) 10 mars 2024
A transformer-based 3D reconstruction model for fast feed-forward 3D generation; it can produce 3D mesh from a single image in under 0.5 seconds; improvement includes better data processing, model design, and training.https://t.co/5VL6kdC12410/ TripoSR A transformer-based 3D reconstruction model for fast feed-forward 3D generation; it can produce 3D mesh from a single image in under 0.5 seconds; improvement includes better data processing, model design, and training.
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Sora Overview: Key Developments in Large Vision Models
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7/ Sora Overview A comprehensive review of Sora and some of the key developments powering this model, including limitations and opportunities of large vision models.
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SaulLM-7B: Legal Domain Language Model
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8/ LLM for Law – introduces SaulLM-7B, an LLM for the legal domain explicitly designed for legal text comprehension and generation; presents an instructional fine-tuning method that leverages legal datasets to further enhance performance in legal tasks.
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RAG for AI-Generated Content: Overview and Enhancements
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5/ RAG for AI-Generated Content Provides an overview of RAG used in different generation scenarios like code, image, and audio, including a taxonomy of RAG enhancements with reference to key papers.