This page shows the most relevant public items for LLMs, ranked by trend activity and review signal. Use weekly for fast changes, monthly for more stable patterns, and all-time for evergreen picks.
Paper • May 9, 2023 • OpenAI • Steven Bills, Nick Cammarata, Dan Mossing, Henk Tillman, Leo Gao, Gabriel Goh, Ilya Sutskever, Jan Leike, Jeff Wu, William Saunders
Understanding the internal mechanisms of massive language models is a critical bottleneck for AI safety and alignment. Given the billions of parameters in modern models, manual human inspection of ...
Paper • Jan 23, 2020 • arXiv • Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B. Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, Dario Amodei
We study empirical scaling laws for language model performance on the cross-entropy loss. The loss scales as a power-law with model size, dataset size, and the amount of compute used for training, ...
Paper • Feb 14, 2019 • OpenAI • Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever
Natural language processing tasks, such as question answering, machine translation, reading comprehension, and summarization, are typically approached with supervised learning on task-specific data...
Paper • May 28, 2020 • arXiv • Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, Dario Amodei
Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typically task-agnostic i...
Paper • Dec 8, 2021 • arXiv • Sebastian Borgeaud, Arthur Mensch, Jordan Hoffmann, Trevor Cai, Eliza Rutherford, Katie Millican, George van den Driessche, Jean-Baptiste Lespiau, Bogdan Damoc, Aidan Clark, Diego de Las Casas, Aurelia Guy, Jacob Menick, Roman Ring, Tom Hennigan, Saffron Huang, Loren Maggiore, Chris Jones, Albin Cassirer, Andy Brock, Michela Paganini, Geoffrey Irving, Oriol Vinyals, Simon Osindero, Karen Simonyan, Jack W. Rae, Erich Elsen, Laurent Sifre
We enhance auto-regressive language models by conditioning on document chunks retrieved from a large corpus, based on local similarity with preceding tokens. With a 2 trillion token database, our R...
Paper • Dec 14, 2023 • Nature • Bernardino Romera-Paredes, Mohammadamin Barekatain, Alexander Novikov, Matej Balog, Pushmeet Kohli
Large language models (LLMs) have demonstrated impressive capabilities in code generation, but their ability to discover novel mathematical knowledge has been limited by hallucinations and lack of ...
Paper • Feb 18, 2026 • bioRxiv • The GHIST Consortium, J. Pool, M. Gutenkunst
Inferring complex evolutionary and demographic histories from genomic data is plagued by model misspecification and unconscious bias. We report the results of the 2026 Genomic History Inference Str...
Paper • Feb 24, 2026 • bioRxiv • Y. Chen, X. Guo, L. Lyu
Automating cell-type annotation in single-cell RNA-seq data using Large Language Models often leads to confident but biologically inaccurate classifications. We present a 'Self-Refining Agent' fram...
Paper • Feb 18, 2026 • bioRxiv • Elena K. Sharma, David Chen, Marcus Thorne, Yujin Han
A critical bottleneck in training autonomous agents for biology is the scarcity of high-throughput experimental data for reward modeling. We introduce rbio1, a framework that trains scientific reas...
Paper • Feb 21, 2024 • arXiv • Gemma Team, Google DeepMind
We introduce Gemma, a family of lightweight, state-of-the-art open models built from the same research and technology used to create the Gemini models. Gemma models are offered in two sizes: a 7 bi...
Paper • Dec 8, 2022 • Science • Yujia Li, David Choi, Junyoung Chung, Nate Kushman, Oriol Vinyals
Programming represents a complex problem-solving task that requires deep logic and algorithmic reasoning. We present AlphaCode, a system that writes computer programs at a competitive level. AlphaC...
Paper • Feb 15, 2026 • arXiv • Zheng et al.
Existing multi-agent frameworks often rely on static or task-level workflows, which either over-process simple queries or underperform on complex ones, while neglecting the efficiency-performance t...
Paper • Mar 15, 2023 • arXiv • OpenAI
We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs. While less capable than humans in many real-world scenarios, GPT...
Paper • Dec 8, 2021 • arXiv • Jack W. Rae, Sebastian Borgeaud, Trevor Cai, Katie Millican, Jordan Hoffmann, Francis Song, John Aslanides, Sarah Henderson, Roman Ring, Susannah Young, Eliza Rutherford, Tom Hennigan, Jacob Menick, Albin Cassirer, Richard Powell, George van den Driessche, Lisa Anne Hendricks, Maribeth Rauh, Po-Sen Huang, Amelia Glaese, Johannes Welbl, Sumanth Dathathri, Saffron Huang, Jonathan Uesato, John Mellor, Irina Higgins, Antonia Creswell, Nat McAleese, Amy Wu, Eleni Elia, Danilo J. Rezende, Vinyals, Simonyan
Language modelling provides a step towards intelligent communication systems by harnessing large datasets and expressive models. We provide an analysis of Transformer-based language model architect...
Paper • Feb 21, 2026 • arXiv • Zhixuan Fang, Yisong Yue, Anima Anandkumar
Long-horizon autonomous agents suffer from 'context rot,' a phenomenon where the accumulation of irrelevant interaction history severely degrades the model's reasoning capabilities over time. We pr...
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