10 citations · 23 across the 7 of their papers we have counts for
7 papers
FSPO: Few-Shot Optimization of Synthetic Preferences Personalizes to Real Users
Anikait Singh, Sheryl Hsu, Kyle Hsu +5
Effective personalization of LLMs is critical for a broad range of user-interfacing applications such as virtual assistants and content curation. Inspired by the strong in-context…
CTC-DRO: Robust Optimization for Reducing Language Disparities in Speech Recognition
Martijn Bartelds, Ananjan Nandi, Moussa Koulako Bala Doumbouya +3
Modern deep learning models often achieve high overall performance, but consistently fail on specific subgroups. Group distributionally robust optimization (group DRO) addresses th…
The Future of Open Human Feedback
Shachar Don-Yehiya, Ben Burtenshaw, Ramon Fernandez Astudillo +17
Human feedback on conversations with language language models (LLMs) is central to how these systems learn about the world, improve their capabilities, and are steered toward desir…
A Survey on Data Selection for Language Models
Alon Albalak, Yanai Elazar, Sang Michael Xie +11
A major factor in the recent success of large language models is the use of enormous and ever-growing text datasets for unsupervised pre-training. However, naively training a model…
Stochastic Amortization: A Unified Approach to Accelerate Feature and Data Attribution
Ian Covert, Chanwoo Kim, Su-In Lee +2
Many tasks in explainable machine learning, such as data valuation and feature attribution, perform expensive computation for each data point and are intractable for large datasets…
MoCa: Measuring Human-Language Model Alignment on Causal and Moral Judgment Tasks
Allen Nie, Yuhui Zhang, Atharva Amdekar +3
Human commonsense understanding of the physical and social world is organized around intuitive theories. These theories support making causal and moral judgments. When something ba…
Navigating the Grey Area: How Expressions of Uncertainty and Overconfidence Affect Language Models
Kaitlyn Zhou, Dan Jurafsky, Tatsunori Hashimoto
The increased deployment of LMs for real-world tasks involving knowledge and facts makes it important to understand model epistemology: what LMs think they know, and how their atti…