Papers by Ofir Press
Improving Transformer Models by Reordering their Sublayers (2020.acl-main)
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| Challenge: | a sandwich transformer pattern is a new approach to multilayer transformers that can be used for different tasks. |
| Approach: | They propose a transformer ordering pattern that reorders sublayers in a sandwich transformer pattern . they generate random transformer models and train them with the language modeling objective . |
| Outcome: | The proposed pattern improves perplexity on multiple word-level and character-level language modeling benchmarks at no cost in parameters, memory, or training time. |
Measuring and Narrowing the Compositionality Gap in Language Models (2023.findings-emnlp)
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| Challenge: | a language model can correctly answer all sub-problems but not generate the overall solution. |
| Approach: | They propose a method that asks itself and then answers follow-up questions to narrow the compositionality gap by reasoning explicitly instead of implicitly. |
| Outcome: | The proposed method improves on chain of thought by asking itself and answering follow-up questions. |
Shortformer: Better Language Modeling using Shorter Inputs (2021.acl-long)
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| Challenge: | Existing methods require computationally expensive relative position embeddings. |
| Approach: | They propose two methods that decrease input length to improve perplexity and perplexability. |
| Outcome: | The proposed methods speed up training by a factor of 1.65 and reduce memory usage. |
What Language Model to Train if You Have One Million GPU Hours? (2022.findings-emnlp)
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Teven Le Scao, Thomas Wang, Daniel Hesslow, Stas Bekman, M Saiful Bari, Stella Biderman, Hady Elsahar, Niklas Muennighoff, Jason Phang, Ofir Press, Colin Raffel, Victor Sanh, Sheng Shen, Lintang Sutawika, Jaesung Tae, Zheng Xin Yong, Julien Launay, Iz Beltagy
| Challenge: | Recent years have seen the advent of large language models characterized by emergent capabilities arising from sheer scale alone. |
| Approach: | They propose to use a multilingual model to compare performance to the English-only model by ablation at the billion-parameter scale. |
| Outcome: | The proposed model is based on a multilingual model and its performance against the English-only model. |
Transformer Language Models without Positional Encodings Still Learn Positional Information (2022.findings-emnlp)
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| Challenge: | Using positional embeddings, Causal transformer language models learn an implicit notion of absolute positions. |
| Approach: | They propose to use positional embeddings to encode positional information in transformer language models. |
| Outcome: | The proposed model learns an implicit notion of absolute positions across datasets, model sizes, and sequence lengths. |
AssistantBench: Can Web Agents Solve Realistic and Time-Consuming Tasks? (2024.emnlp-main)
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| Challenge: | Current language models and retrieval-augmented LMs are limited in their ability to perform tasks on the web. |
| Approach: | They propose a benchmark to evaluate language agents built on top of language models . they propose 'AssistantBench' which includes 214 tasks that can be automatically evaluated . |
| Outcome: | The proposed agent outperforms existing agents in a new benchmark for language agents on the web. |