4/ Introduction to Vision-Language Modeling – presents an introduction to vision-language models along with key details of how they work and how to effectively train these models.
@dair_ai
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Top ML Papers of the Week: SimPO, GNN-RAG, and More
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The Top ML Papers of the Week (May 27 – June 2): – SimPO
– GNN-RAG
– Attention as an RNN
– Abacus Embeddings
– Symbolic Chain-of-Thought
– Contextual Position Encoding
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Contextual Position Encoding: New Method for Transformer Models
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1/ Contextual Position Encoding – proposes a new position encoding method, CoPE, to enable the position to be conditioned on context by incrementing position only on certain tokens…
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Efficient Multimodal LLMs: Survey of Structures and Applications
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10/ Efficient Multimodal LLMs – provides a comprehensive and systematic survey of the current state of efficient multimodal large language models; discusses efficient structures and strategies, applications, limitations, and promising future directions.
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DeepSeek-Prover: AI Model Generates Lean 4 Mathematical Proofs
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9/ DeepSeek-Prover – introduces an approach to generate Lean 4 proof data from high-school and undergraduate-level mathematical competition problems; it uses the synthetic data, comprising of 8 million formal statements and proofs, to fine-tune a DeepSeekMath 7B model…
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INDUS: Comprehensive LLM Suite for Scientific Earth and Planetary Research
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8/ Scientific Applications of LLMs – presents INDUS, a comprehensive suite of LLMs for Earth science, biology, physics, planetary sciences, and more; includes an encoder model, embedding model, and small distilled models.
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Layer-Condensed KV Cache Optimizes LLM Inference Efficiency
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6/ Efficient LLM Inference – proposes a layer-condensed KV cache to achieve efficient inference in LLMs; only computes and caches the key-values (KVs) of a small number of layers which leads to saving memory consumption and improved inference throughput.
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Guide for Evaluating Large Language Models with Open-Source Library
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7/ Guide for Evaluating LLMs – provides guidance and lessons for evaluating large language models; discusses challenges and best practices, along with the introduction of an open-source library for evaluating LLMs.
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Hierarchical Reasoning Aggregation Framework for LLM Answer Selection
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4/ Enhancing Answer Selection in LLMs – proposes a hierarchical reasoning aggregation framework for improving the reasoning capabilities of LLMs; the approach selects answers based on the evaluation of reasoning chains.
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How Far Are We From Achieving AGI
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5/ How Far Are We From AGI – presents an opinion paper addressing important questions to understand the proximity to artificial general intelligence (AGI).