96 citations · 125 across the 6 of their papers we have counts for
7 papers
Collaborative Disagreement Resolution for Scalable Oversight
Yuyang Jiang, Chacha Chen, Teng Wu +4
Debate, where AI agents argue opposing positions, has emerged as a key approach to scalable oversight. However, debate faces a fundamental tension: models are incentivized to be pe…
Beyond Reverse KL: Generalizing Direct Preference Optimization with Diverse Divergence Constraints
Chaoqi Wang, Yibo Jiang, Chenghao Yang +2
The increasing capabilities of large language models (LLMs) raise opportunities for artificial general intelligence but concurrently amplify safety concerns, such as potential misu…
Learning Human-Compatible Representations for Case-Based Decision Support
Han Liu, Yizhou Tian, Chacha Chen +3
Algorithmic case-based decision support provides examples to help human make sense of predicted labels and aid human in decision-making tasks. Despite the promising performance of…
Understanding the Effect of Out-of-distribution Examples and Interactive Explanations on Human-AI Decision Making
Han Liu, Vivian Lai, Chenhao Tan
Although AI holds promise for improving human decision making in societally critical domains, it remains an open question how human-AI teams can reliably outperform AI alone and hu…
Morphology Matters: A Multilingual Language Modeling Analysis
Hyunji Hayley Park, Katherine J. Zhang, Coleman Haley +3
Prior studies in multilingual language modeling (e.g., Cotterell et al., 2018; Mielke et al., 2019) disagree on whether or not inflectional morphology makes languages harder to mod…
Neural Polysynthetic Language Modelling
Lane Schwartz, Francis Tyers, Lori Levin +18
Research in natural language processing commonly assumes that approaches that work well for English and and other widely-used languages are "language agnostic". In high-resource la…
"Why is 'Chicago' deceptive?" Towards Building Model-Driven Tutorials for Humans
Vivian Lai, Han Liu, Chenhao Tan
To support human decision making with machine learning models, we often need to elucidate patterns embedded in the models that are unsalient, unknown, or counterintuitive to humans…