65 citations · 167 across the 9 of their papers we have counts for
5 papers · 1 filter
The Bias Amplification Paradox in Text-to-Image Generation
Preethi Seshadri, Sameer Singh, Yanai Elazar
Bias amplification is a phenomenon in which models exacerbate biases or stereotypes present in the training data. In this paper, we study bias amplification in the text-to-image do…
Selective Perception: Optimizing State Descriptions with Reinforcement Learning for Language Model Actors
Kolby Nottingham, Yasaman Razeghi, Kyungmin Kim +4
Large language models (LLMs) are being applied as actors for sequential decision making tasks in domains such as robotics and games, utilizing their general world knowledge and pla…
PURR: Efficiently Editing Language Model Hallucinations by Denoising Language Model Corruptions
Anthony Chen, Panupong Pasupat, Sameer Singh +2
The remarkable capabilities of large language models have been accompanied by a persistent drawback: the generation of false and unsubstantiated claims commonly known as "hallucina…
TABLET: Learning From Instructions For Tabular Data
Dylan Slack, Sameer Singh
Acquiring high-quality data is often a significant challenge in training machine learning (ML) models for tabular prediction, particularly in privacy-sensitive and costly domains l…
ART: Automatic multi-step reasoning and tool-use for large language models
Bhargavi Paranjape, Scott Lundberg, Sameer Singh +3
Large language models (LLMs) can perform complex reasoning in few- and zero-shot settings by generating intermediate chain of thought (CoT) reasoning steps. Further, each reasoning…