16 citations · 22 across the 11 of their papers we have counts for
12 papers
Multimodal QUD: Inquisitive Questions from Scientific Figures
Yating Wu, William Rudman, Venkata S Govindarajan +2
Discourse comprehension in complex documents often involves continuously posing and resolving Questions Under Discussion (QUDs). While QUD frameworks have so far focused on text, s…
Dark & Stormy: Modeling Humor in Sentences from the Bulwer-Lytton Fiction Contest
Venkata S Govindarajan, Laura Biester
Textual humor is enormously diverse and computational studies need to account for this range, including intentionally bad humor. In this paper, we curate and analyze a novel corpus…
Measuring Lexical Diversity of Synthetic Data Generated through Fine-Grained Persona Prompting
Gauri Kambhatla, Chantal Shaib, Venkata Govindarajan
Fine-grained personas have recently been used for generating 'diverse' synthetic data for pre-training and supervised fine-tuning of Large Language Models (LLMs). In this work, we…
Do they mean 'us'? Interpreting Referring Expressions in Intergroup Bias
Venkata S Govindarajan, Matianyu Zang, Kyle Mahowald +2
The variations between in-group and out-group speech (intergroup bias) are subtle and could underlie many social phenomena like stereotype perpetuation and implicit bias. In this p…
Counterfactually Probing Language Identity in Multilingual Models
Anirudh Srinivasan, Venkata S Govindarajan, Kyle Mahowald
Techniques in causal analysis of language models illuminate how linguistic information is organized in LLMs. We use one such technique, AlterRep, a method of counterfactual probing…
Lil-Bevo: Explorations of Strategies for Training Language Models in More Humanlike Ways
Venkata S Govindarajan, Juan Diego Rodriguez, Kaj Bostrom +1
We present Lil-Bevo, our submission to the BabyLM Challenge. We pretrained our masked language models with three ingredients: an initial pretraining with music data, training on sh…
Counterfactual Probing for the Influence of Affect and Specificity on Intergroup Bias
Venkata S Govindarajan, Kyle Mahowald, David I. Beaver +1
While existing work on studying bias in NLP focues on negative or pejorative language use, Govindarajan et al. (2023) offer a revised framing of bias in terms of intergroup social…
How people talk about each other: Modeling Generalized Intergroup Bias and Emotion
Venkata S Govindarajan, Katherine Atwell, Barea Sinno +3
Current studies of bias in NLP rely mainly on identifying (unwanted or negative) bias towards a specific demographic group. While this has led to progress recognizing and mitigatin…
longhorns at DADC 2022: How many linguists does it take to fool a Question Answering model? A systematic approach to adversarial attacks
Venelin Kovatchev, Trina Chatterjee, Venkata S Govindarajan +9
Developing methods to adversarially challenge NLP systems is a promising avenue for improving both model performance and interpretability. Here, we describe the approach of the tea…
Help! Need Advice on Identifying Advice
Venkata Subrahmanyan Govindarajan, Benjamin T Chen, Rebecca Warholic +2
Humans use language to accomplish a wide variety of tasks - asking for and giving advice being one of them. In online advice forums, advice is mixed in with non-advice, like emotio…
The Universal Decompositional Semantics Dataset and Decomp Toolkit
Aaron Steven White, Elias Stengel-Eskin, Siddharth Vashishtha +9
We present the Universal Decompositional Semantics (UDS) dataset (v1.0), which is bundled with the Decomp toolkit (v0.1). UDS1.0 unifies five high-quality, decompositional semantic…
Decomposing Generalization: Models of Generic, Habitual, and Episodic Statements
Venkata Subrahmanyan Govindarajan, Benjamin Van Durme, Aaron Steven White
We present a novel semantic framework for modeling linguistic expressions of generalization---generic, habitual, and episodic statements---as combinations of simple, real-valued re…