12 citations · 22 across the 9 of their papers we have counts for
10 papers
The Effect of Perceived Race and Gender on Police Language Use: Experimental Evidence from VR Simulations
Sandra C. Sandoval, Navita Goyal, Rashawn Ray +3
Against the backdrop of violence in police interactions with the U.S. public, we explore how deferentially police officers speak to virtual characters depicted as Black adult males…
Steering Safely or Off a Cliff? Rethinking Specificity and Robustness in Inference-Time Interventions
Navita Goyal, Hal Daumé
Model steering, which involves intervening on hidden representations at inference time, has emerged as a lightweight alternative to finetuning for precisely controlling large langu…
Causal Effect of Group Diversity on Redundancy and Coverage in Peer-Reviewing
Navita Goyal, Ivan Stelmakh, Nihar Shah +1
A large host of scientific journals and conferences solicit peer reviews from multiple reviewers for the same submission, aiming to gather a broader range of perspectives and mitig…
Explaining with Contrastive Phrasal Highlighting: A Case Study in Assisting Humans to Detect Translation Differences
Eleftheria Briakou, Navita Goyal, Marine Carpuat
Explainable NLP techniques primarily explain by answering "Which tokens in the input are responsible for this prediction?''. We argue that for NLP models that make predictions by c…
Large Language Models Help Humans Verify Truthfulness -- Except When They Are Convincingly Wrong
Chenglei Si, Navita Goyal, Sherry Tongshuang Wu +4
Large Language Models (LLMs) are increasingly used for accessing information on the web. Their truthfulness and factuality are thus of great interest. To help users make the right…
The Impact of Explanations on Fairness in Human-AI Decision-Making: Protected vs Proxy Features
Navita Goyal, Connor Baumler, Tin Nguyen +1
AI systems have been known to amplify biases in real-world data. Explanations may help human-AI teams address these biases for fairer decision-making. Typically, explanations focus…
What Else Do I Need to Know? The Effect of Background Information on Users' Reliance on QA Systems
Navita Goyal, Eleftheria Briakou, Amanda Liu +6
NLP systems have shown impressive performance at answering questions by retrieving relevant context. However, with the increasingly large models, it is impossible and often undesir…
Personalized Detection of Cognitive Biases in Actions of Users from Their Logs: Anchoring and Recency Biases
Atanu R Sinha, Navita Goyal, Sunny Dhamnani +4
Cognitive biases are mental shortcuts humans use in dealing with information and the environment, and which result in biased actions and behaviors (or, actions), unbeknownst to the…
CaM-Gen:Causally-aware Metric-guided Text Generation
Navita Goyal, Roodram Paneri, Ayush Agarwal +3
Content is created for a well-defined purpose, often described by a metric or signal represented in the form of structured information. The relationship between the goal (metrics)…
Multi-Style Transfer with Discriminative Feedback on Disjoint Corpus
Navita Goyal, Balaji Vasan Srinivasan, Anandhavelu Natarajan +1
Style transfer has been widely explored in natural language generation with non-parallel corpus by directly or indirectly extracting a notion of style from source and target domain…