565 citations · 1.1k across the 11 of their papers we have counts for
10 papers
On the Role of Citations in Preference Data
Yu Hou, Hal Daumé, Rachel Rudinger +1
Many NLP tasks require systems to provide attribution in their outputs--i.e. citations to grounding sources. Attribution serves as a bulwark against model hallucination and as a me…
AI, Take the Wheel: What Drives Delegation and Trust in Human-Computer Cooperative Question Answering?
Maharshi Gor, Yoo Yeon Sung, Yu Hou +4
AI systems are fallible, and humans can make mistakes in deciding whether to trust AI over their own judgment. Thus, improving human-AI collaboration requires understanding when, w…
Language Models Predict Empathy Gaps Between Social In-groups and Out-groups
Yu Hou, Hal Daumé, Rachel Rudinger
Studies of human psychology have demonstrated that people are more motivated to extend empathy to in-group members than out-group members (Cikara et al., 2011). In this study, we i…
GRACE: A Granular Benchmark for Evaluating Model Calibration against Human Calibration
Yoo Yeon Sung, Eve Fleisig, Yu Hou +2
Language models are often miscalibrated, leading to confidently incorrect answers. We introduce GRACE, a benchmark for language model calibration that incorporates comparison with…
Natural Language Inference Improves Compositionality in Vision-Language Models
Paola Cascante-Bonilla, Yu Hou, Yang Trista Cao +2
Compositional reasoning in Vision-Language Models (VLMs) remains challenging as these models often struggle to relate objects, attributes, and spatial relationships. Recent methods…
ACQUIRED: A Dataset for Answering Counterfactual Questions In Real-Life Videos
Te-Lin Wu, Zi-Yi Dou, Qingyuan Hu +5
Multimodal counterfactual reasoning is a vital yet challenging ability for AI systems. It involves predicting the outcomes of hypothetical circumstances based on vision and languag…
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao +448
Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabil…
Are Akpans Trick or Treat: Unveiling Helpful Biases in Assistant Systems
Jiao Sun, Yu Hou, Jiin Kim +1
Information-seeking AI assistant systems aim to answer users' queries about knowledge in a timely manner. However, both the human-perceived helpfulness of information-seeking assis…
On Measures of Biases and Harms in NLP
Sunipa Dev, Emily Sheng, Jieyu Zhao +8
Recent studies show that Natural Language Processing (NLP) technologies propagate societal biases about demographic groups associated with attributes such as gender, race, and nati…
COM2SENSE: A Commonsense Reasoning Benchmark with Complementary Sentences
Shikhar Singh, Nuan Wen, Yu Hou +4
Commonsense reasoning is intuitive for humans but has been a long-term challenge for artificial intelligence (AI). Recent advancements in pretrained language models have shown prom…