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Large Language Models

The BigCode Project Governance Card
The BigCode Project Governance Card
This document serves as an overview of the different mechanisms and areas of governance in the BigCode project. It aims to support …
ArXiv,  2024.
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Capture the Flag: Uncovering Data Insights with Large Language Models
Capture the Flag: Uncovering Data Insights with Large Language Models
The extraction of a small number of relevant insights from vast amounts of data is a crucial component of data-driven decision-making. …
Workshop at the Neural Information Processing Systems (NeurIPS),  2023.
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Lag-Llama: A Foundation Model for Probabilistic Time Series Forecasting
Lag-Llama: A Foundation Model for Probabilistic Time Series Forecasting
In this work, we present Lag-Llama, a general-purpose probabilistic time series forecasting model trained on a large collection of time …
Workshop at the Neural Information Processing Systems (NeurIPS),  2023.
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The Unsolved Challenges of LLMs in Open-Ended Web Tasks: A Case Study
The Unsolved Challenges of LLMs in Open-Ended Web Tasks: A Case Study
In this work, we investigate the challenges associated with developing goal-driven AI agents capable of performing open-ended tasks in …
Workshop at the Neural Information Processing Systems (NeurIPS),  2023.
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LLM aided semi-supervision for efficient Extractive Dialog Summarization
LLM aided semi-supervision for efficient Extractive Dialog Summarization
Generating high-quality summaries for chat dialogs often requires large labeled datasets. We propose a method to efficiently use …
Conference on Empirical Methods in Natural Language Processing (EMNLP),  2023.
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MAGNIFICO: Evaluating the In-Context Learning Ability of Large Language Models to Generalize to Novel Interpretations
MAGNIFICO: Evaluating the In-Context Learning Ability of Large Language Models to Generalize to Novel Interpretations
Humans possess a remarkable ability to assign novel interpretations to linguistic expressions, enabling them to learn new words and …
Conference on Empirical Methods in Natural Language Processing (EMNLP),  2023.
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PromptMix: A Class Boundary Augmentation Method for Large Language Model Distillation
PromptMix: A Class Boundary Augmentation Method for Large Language Model Distillation
Data augmentation is a widely used technique to address the problem of text classification when there is a limited amount of training …
Conference on Empirical Methods in Natural Language Processing (EMNLP),  2023.
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Equivariant Adaptation of Large Pre-Trained Models
Equivariant Adaptation of Large Pre-Trained Models
Equivariant networks are specifically designed to ensure consistent behavior with respect to a set of input transformations, leading to …
Conference on Neural Information Processing Systems (NeurIPS),  2023.
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Causal Discovery with Language Models as Imperfect Experts
Causal Discovery with Language Models as Imperfect Experts
Understanding the causal relationships that underlie a system is a fundamental prerequisite to accurate decision-making. In this work, …
Workshop on Structured Probabilistic Inference & Generative Modeling (ICML),  2023.
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StarCoder: may the source be with you!
StarCoder: may the source be with you!
The BigCode community, an open-scientific collaboration working on the responsible development of Large Language Models for Code (Code …
Raymond Li, Loubna Ben Allal, Yangtian Zi, Denis Kocetkov, Chenghao Mou, Christopher Akiki, Jia Li, Jenny Chim, Terry Yue Zhuo, Thomas Wang, Mishig Davaadorj, João Monteiro, Oleh Shliazhko, Nicolas Gontier, Nicholas Meade, Ming-Ho Yee, Logesh Kumar Umapathi, Benjamin Lipkin, Zhiruo Wang, Rudra Murthy, Jason Stillerman, Siva Sankalp Patel, Dmitry Abulkhanov, Marco Zocca, Zhihan Zhang, Nour Fahmy, Urvashi Bhattacharyya, Swayam Singh, Sasha Luccioni, Paulo Villegas, Maxim Kunakov, Fedor Zhdanov, Manuel Romero, Tony Lee, Nadav Timor, Jennifer Ding, Claire Schlesinger, Hailey Schoelkopf, Jan Ebert, Jennifer Robinson, Carolyn Jane Anderson, Brendan Dolan-Gavitt, Danish Contractor, Siva Reddy, Daniel Fried, Dzmitry Bahdanau, Yacine Jernite, Carlos Muñoz Ferrandis, Sean Hughes, Thomas Wolf , Arjun Guha, Leandro von Werra, Harm de Vries, Joel Lamy Poirier, Alex Gu, Armel Zebaze, Jian Zhu, Manan Dey, Marc Marone, Mayank Mishra, Muhtasham Oblokulov, Olivier Dehaene, Qian Liu, Tri Dao, Wenhao Yu, Niklas Muennighoff
Transactions on Machine Learning Research (TMLR),  2023.
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