12 citations · 22 across the 9 of their papers we have counts for
4 papers · 1 filter
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…