Arjun Subramonian


2023

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Prompting Multilingual Large Language Models to Generate Code-Mixed Texts: The Case of South East Asian Languages
Zheng Xin Yong | Ruochen Zhang | Jessica Forde | Skyler Wang | Arjun Subramonian | Holy Lovenia | Samuel Cahyawijaya | Genta Winata | Lintang Sutawika | Jan Christian Blaise Cruz | Yin Lin Tan | Long Phan | Long Phan | Rowena Garcia | Thamar Solorio | Alham Fikri Aji
Proceedings of the 6th Workshop on Computational Approaches to Linguistic Code-Switching

The differences in decision making between behavioural models of voice interfaces are hard to capture using existing measures for the absolute performance of such models. For instance, two models may have a similar task success rate, but very different ways of getting there. In this paper, we propose a general methodology to compute the similarity of two dialogue behaviour models and investigate different ways of computing scores on both the semantic and the textual level. Complementing absolute measures of performance, we test our scores on three different tasks and show the practical usability of the measures.

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It Takes Two to Tango: Navigating Conceptualizations of NLP Tasks and Measurements of Performance
Arjun Subramonian | Xingdi Yuan | Hal Daumé III | Su Lin Blodgett
Findings of the Association for Computational Linguistics: ACL 2023

Progress in NLP is increasingly measured through benchmarks; hence, contextualizing progress requires understanding when and why practitioners may disagree about the validity of benchmarks. We develop a taxonomy of disagreement, drawing on tools from measurement modeling, and distinguish between two types of disagreement: 1) how tasks are conceptualized and 2) how measurements of model performance are operationalized. To provide evidence for our taxonomy, we conduct a meta-analysis of relevant literature to understand how NLP tasks are conceptualized, as well as a survey of practitioners about their impressions of different factors that affect benchmark validity. Our meta-analysis and survey across eight tasks, ranging from coreference resolution to question answering, uncover that tasks are generally not clearly and consistently conceptualized and benchmarks suffer from operationalization disagreements. These findings support our proposed taxonomy of disagreement. Finally, based on our taxonomy, we present a framework for constructing benchmarks and documenting their limitations.

2022

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You reap what you sow: On the Challenges of Bias Evaluation Under Multilingual Settings
Zeerak Talat | Aurélie Névéol | Stella Biderman | Miruna Clinciu | Manan Dey | Shayne Longpre | Sasha Luccioni | Maraim Masoud | Margaret Mitchell | Dragomir Radev | Shanya Sharma | Arjun Subramonian | Jaesung Tae | Samson Tan | Deepak Tunuguntla | Oskar Van Der Wal
Proceedings of BigScience Episode #5 -- Workshop on Challenges & Perspectives in Creating Large Language Models

Evaluating bias, fairness, and social impact in monolingual language models is a difficult task. This challenge is further compounded when language modeling occurs in a multilingual context. Considering the implication of evaluation biases for large multilingual language models, we situate the discussion of bias evaluation within a wider context of social scientific research with computational work. We highlight three dimensions of developing multilingual bias evaluation frameworks: (1) increasing transparency through documentation, (2) expanding targets of bias beyond gender, and (3) addressing cultural differences that exist between languages. We further discuss the power dynamics and consequences of training large language models and recommend that researchers remain cognizant of the ramifications of developing such technologies.

2021

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Harms of Gender Exclusivity and Challenges in Non-Binary Representation in Language Technologies
Sunipa Dev | Masoud Monajatipoor | Anaelia Ovalle | Arjun Subramonian | Jeff Phillips | Kai-Wei Chang
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Gender is widely discussed in the context of language tasks and when examining the stereotypes propagated by language models. However, current discussions primarily treat gender as binary, which can perpetuate harms such as the cyclical erasure of non-binary gender identities. These harms are driven by model and dataset biases, which are consequences of the non-recognition and lack of understanding of non-binary genders in society. In this paper, we explain the complexity of gender and language around it, and survey non-binary persons to understand harms associated with the treatment of gender as binary in English language technologies. We also detail how current language representations (e.g., GloVe, BERT) capture and perpetuate these harms and related challenges that need to be acknowledged and addressed for representations to equitably encode gender information.