OLMo: Accelerating the Science of Language Models

2 code implementations • 1 Feb 2024 • Dirk Groeneveld, Iz Beltagy, Pete Walsh, Akshita Bhagia, Rodney Kinney, Oyvind Tafjord, Ananya Harsh Jha, Hamish Ivison, Ian Magnusson, Yizhong Wang, Shane Arora, David Atkinson, Russell Authur, Khyathi Raghavi Chandu, Arman Cohan, Jennifer Dumas, Yanai Elazar, Yuling Gu, Jack Hessel, Tushar Khot, William Merrill, Jacob Morrison, Niklas Muennighoff, Aakanksha Naik, Crystal Nam, Matthew E. Peters, Valentina Pyatkin, Abhilasha Ravichander, Dustin Schwenk, Saurabh Shah, Will Smith, Emma Strubell, Nishant Subramani, Mitchell Wortsman, Pradeep Dasigi, Nathan Lambert, Kyle Richardson, Luke Zettlemoyer, Jesse Dodge, Kyle Lo, Luca Soldaini, Noah A. Smith, Hannaneh Hajishirzi

Given the importance of these details in scientifically studying these models, including their biases and potential risks, we believe it is essential for the research community to have access to powerful, truly open LMs.

Language Modelling

Camels in a Changing Climate: Enhancing LM Adaptation with Tulu 2

2 code implementations • 17 Nov 2023 • Hamish Ivison, Yizhong Wang, Valentina Pyatkin, Nathan Lambert, Matthew Peters, Pradeep Dasigi, Joel Jang, David Wadden, Noah A. Smith, Iz Beltagy, Hannaneh Hajishirzi

Since the release of T\"ULU [Wang et al., 2023b], open resources for instruction tuning have developed quickly, from better base models to new finetuning techniques.

How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources

1 code implementation • NeurIPS 2023 • Yizhong Wang, Hamish Ivison, Pradeep Dasigi, Jack Hessel, Tushar Khot, Khyathi Raghavi Chandu, David Wadden, Kelsey MacMillan, Noah A. Smith, Iz Beltagy, Hannaneh Hajishirzi

Our evaluations show that the best model in any given evaluation reaches on average 87% of ChatGPT performance, and 73% of GPT-4 performance, suggesting that further investment in building better base models and instruction-tuning data is required to close the gap.

Instruction Following

TESS: Text-to-Text Self-Conditioned Simplex Diffusion

1 code implementation • 15 May 2023 • Rabeeh Karimi Mahabadi, Hamish Ivison, Jaesung Tae, James Henderson, Iz Beltagy, Matthew E. Peters, Arman Cohan

Diffusion models have emerged as a powerful paradigm for generation, obtaining strong performance in various continuous domains.

Natural Language Understanding Paraphrase Generation +3

HINT: Hypernetwork Instruction Tuning for Efficient Zero- & Few-Shot Generalisation

no code implementations • 20 Dec 2022 • Hamish Ivison, Akshita Bhagia, Yizhong Wang, Hannaneh Hajishirzi, Matthew Peters

By converting instructions into modules, HINT models can effectively disregard the length of instructions and few-shot example inputs in terms of compute usage.

In-Context Learning

Data-Efficient Finetuning Using Cross-Task Nearest Neighbors

1 code implementation • 1 Dec 2022 • Hamish Ivison, Noah A. Smith, Hannaneh Hajishirzi, Pradeep Dasigi

Obtaining labeled data to train a model for a task of interest is often expensive.

Hyperdecoders: Instance-specific decoders for multi-task NLP

1 code implementation • 15 Mar 2022 • Hamish Ivison, Matthew E. Peters

We investigate input-conditioned hypernetworks for multi-tasking in NLP, generating parameter-efficient adaptations for a decoder using a hypernetwork conditioned on the output of an encoder.

Local Interpretations for Explainable Natural Language Processing: A Survey

no code implementations • 20 Mar 2021 • Siwen Luo, Hamish Ivison, Caren Han, Josiah Poon

As the use of deep learning techniques has grown across various fields over the past decade, complaints about the opaqueness of the black-box models have increased, resulting in an increased focus on transparency in deep learning models.

Machine Translation Sentiment Analysis +1