13 citations · 24 across the 11 of their papers we have counts for
11 papers
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…
CharED: Character-wise Ensemble Decoding for Large Language Models
Kevin Gu, Eva Tuecke, Dmitriy Katz +3
Large language models (LLMs) have shown remarkable potential for problem solving, with open source models achieving increasingly impressive performance on benchmarks measuring area…
Asymmetry in Low-Rank Adapters of Foundation Models
Jiacheng Zhu, Kristjan Greenewald, Kimia Nadjahi +6
Parameter-efficient fine-tuning optimizes large, pre-trained foundation models by updating a subset of parameters; in this class, Low-Rank Adaptation (LoRA) is particularly effecti…
Uncertainty Quantification via Stable Distribution Propagation
Felix Petersen, Aashwin Mishra, Hilde Kuehne +3
We propose a new approach for propagating stable probability distributions through neural networks. Our method is based on local linearization, which we show to be an optimal appro…
GeRA: Label-Efficient Geometrically Regularized Alignment
Dustin Klebe, Tal Shnitzer, Mikhail Yurochkin +2
Pretrained unimodal encoders incorporate rich semantic information into embedding space structures. To be similarly informative, multi-modal encoders typically require massive amou…
An Investigation of Representation and Allocation Harms in Contrastive Learning
Subha Maity, Mayank Agarwal, Mikhail Yurochkin +1
The effect of underrepresentation on the performance of minority groups is known to be a serious problem in supervised learning settings; however, it has been underexplored so far…