Brandon M. Stewart

Also published as: Brandon Stewart


2023

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GPT Deciphering Fedspeak: Quantifying Dissent Among Hawks and Doves
Denis Peskoff | Adam Visokay | Sander Schulhoff | Benjamin Wachspress | Alan Blinder | Brandon Stewart
Findings of the Association for Computational Linguistics: EMNLP 2023

Markets and policymakers around the world hang on the consequential monetary policy decisions made by the Federal Open Market Committee (FOMC). Publicly available textual documentation of their meetings provides insight into members’ attitudes about the economy. We use GPT-4 to quantify dissent among members on the topic of inflation. We find that transcripts and minutes reflect the diversity of member views about the macroeconomic outlook in a way that is lost or omitted from the public statements. In fact, diverging opinions that shed light upon the committee’s “true” attitudes are almost entirely omitted from the final statements. Hence, we argue that forecasting FOMC sentiment based solely on statements will not sufficiently reflect dissent among the hawks and doves.

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Credible without Credit: Domain Experts Assess Generative Language Models
Denis Peskoff | Brandon Stewart
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Language models have recently broken into the public consciousness with the release of the wildly popular ChatGPT. Commentators have argued that language models could replace search engines, make college essays obsolete, or even write academic research papers. All of these tasks rely on accuracy of specialized information which can be difficult to assess for non-experts. Using 10 domain experts across science and culture, we provide an initial assessment of the coherence, conciseness, accuracy, and sourcing of two language models across 100 expert-written questions. While we find the results are consistently cohesive and concise, we find that they are mixed in their accuracy. These results raise questions of the role language models should play in general-purpose and expert knowledge seeking.

2022

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Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond
Amir Feder | Katherine A. Keith | Emaad Manzoor | Reid Pryzant | Dhanya Sridhar | Zach Wood-Doughty | Jacob Eisenstein | Justin Grimmer | Roi Reichart | Margaret E. Roberts | Brandon M. Stewart | Victor Veitch | Diyi Yang
Transactions of the Association for Computational Linguistics, Volume 10

A fundamental goal of scientific research is to learn about causal relationships. However, despite its critical role in the life and social sciences, causality has not had the same importance in Natural Language Processing (NLP), which has traditionally placed more emphasis on predictive tasks. This distinction is beginning to fade, with an emerging area of interdisciplinary research at the convergence of causal inference and language processing. Still, research on causality in NLP remains scattered across domains without unified definitions, benchmark datasets and clear articulations of the challenges and opportunities in the application of causal inference to the textual domain, with its unique properties. In this survey, we consolidate research across academic areas and situate it in the broader NLP landscape. We introduce the statistical challenge of estimating causal effects with text, encompassing settings where text is used as an outcome, treatment, or to address confounding. In addition, we explore potential uses of causal inference to improve the robustness, fairness, and interpretability of NLP models. We thus provide a unified overview of causal inference for the NLP community.1

2021

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Proceedings of the First Workshop on Causal Inference and NLP
Amir Feder | Katherine Keith | Emaad Manzoor | Reid Pryzant | Dhanya Sridhar | Zach Wood-Doughty | Jacob Eisenstein | Justin Grimmer | Roi Reichart | Molly Roberts | Uri Shalit | Brandon Stewart | Victor Veitch | Diyi Yang
Proceedings of the First Workshop on Causal Inference and NLP

2018

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A La Carte Embedding: Cheap but Effective Induction of Semantic Feature Vectors
Mikhail Khodak | Nikunj Saunshi | Yingyu Liang | Tengyu Ma | Brandon Stewart | Sanjeev Arora
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Motivations like domain adaptation, transfer learning, and feature learning have fueled interest in inducing embeddings for rare or unseen words, n-grams, synsets, and other textual features. This paper introduces a la carte embedding, a simple and general alternative to the usual word2vec-based approaches for building such representations that is based upon recent theoretical results for GloVe-like embeddings. Our method relies mainly on a linear transformation that is efficiently learnable using pretrained word vectors and linear regression. This transform is applicable on the fly in the future when a new text feature or rare word is encountered, even if only a single usage example is available. We introduce a new dataset showing how the a la carte method requires fewer examples of words in context to learn high-quality embeddings and we obtain state-of-the-art results on a nonce task and some unsupervised document classification tasks.

2015

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TopicCheck: Interactive Alignment for Assessing Topic Model Stability
Jason Chuang | Margaret E. Roberts | Brandon M. Stewart | Rebecca Weiss | Dustin Tingley | Justin Grimmer | Jeffrey Heer
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2013

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Learning to Extract International Relations from Political Context
Brendan O’Connor | Brandon M. Stewart | Noah A. Smith
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)