A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
John M. Dagdelen, Alexander Dunn, Sang‐Hoon Lee, et al.
Fine-tuning pretrained LLMs (GPT-3, Llama-2) enables joint named entity recognition and relation extraction from scientific text, producing structured records for materials chemistry.
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The paper examines four prominent large language models used in education, including GPT-4 and LLaMA, and outlines their respective limitations.
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Proposes Lag-Llama, a univariate probabilistic time-series forecasting model designed for scaling law analyses of time series foundation models.
Baptiste Rozière, Jonas Gehring, Fabian Gloeckle, et al.
Code Llama is a family of open-source LLMs for code based on Llama 2, achieving state-of-the-art performance in code generation, infilling, and long-context tasks.
Shen Nie, Fengqi Zhu, Zebin You, et al.
LLaDA is a diffusion model trained from scratch for language modeling that matches autoregressive LLMs like LLaMA3 8B across benchmarks and solves the reversal curse.
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An open-source family of heterogeneous reasoning models (Nano, Super, Ultra) with dynamic reasoning toggle, trained via NAS, distillation, and RL.
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Tulu V3 presents a fully open post-training recipe for Llama 3.1 models, achieving state-of-the-art performance via SFT, DPO, and RLVR.
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Meta's quantized Llama 3.2 and Llama 3.3 models reduce size and memory via QAT with LoRA and SpinQuant, enabling deployment on mobile devices.
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Introduces small and medium-sized vision LLMs (11B and 90B) alongside lightweight text-only models (1B and 3B).
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Hermes 3 introduces neutrally-aligned instruct and tool-use models fine-tuned from Llama 3.1, prioritizing strict adherence to user prompts without moral judgment.
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LLM Compiler is a suite of pre-trained models built on Code Llama for code optimization, including LLVM-IR assembly/disassembly and code size reduction.
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Llama 3.1 adds multimodal capabilities via compositional cross-attention adapters for vision and speech, achieving competitive performance with GPT-4V and strong video reasoning.