Adina Williams

Information-Theoretic Probing for Linguistic Structure

2020

The success of neural networks on a diverse set of NLP tasks has led researchers to question how much these networks actually "know" about natural language. Probes are a natural way of assessing this. When …

UnNatural Language Inference

2021

Koustuv Sinha, Prasanna Parthasarathi, Joelle Pineau, Adina Williams. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.

Masked Language Modeling and the Distributional Hypothesis: Order Word\n Matters Pre-training for Little

2021 · arXiv (Cornell University)

A possible explanation for the impressive performance of masked language\nmodel (MLM) pre-training is that such models have learned to represent the\nsyntactic structures prevalent in classical NLP pipelines. In this paper, we\npropose a different explanation: MLMs …

A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference

2018

Adina Williams, Nikita Nangia, Samuel Bowman. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). 2018.

Do latent tree learning models identify meaningful structure in sentences?

2018 · Transactions of the Association for Computational Linguistics

Recent work on the problem of latent tree learning has made it possible to train neural networks that learn to both parse a sentence and use the resulting parse to interpret the sentence, all without …

XNLI: Evaluating Cross-lingual Sentence Representations

2018 · arXiv (Cornell University)

Alexis Conneau, Ruty Rinott, Guillaume Lample, Adina Williams, Samuel Bowman, Holger Schwenk, Veselin Stoyanov. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 2018.

Masked Language Modeling and the Distributional Hypothesis: Order Word Matters Pre-training for Little

2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

A possible explanation for the impressive performance of masked language model (MLM) pre-training is that such models have learned to represent the syntactic structures prevalent in classical NLP pipelines. In this paper, we propose a …

Llama 2: Open Foundation and Fine-Tuned Chat Models

2023 · arXiv (Cornell University)

In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama 2-Chat, …