565 citations · 1.2k across the 57 of their papers we have counts for
6 papers · 2 filters
AssistantBench: Can Web Agents Solve Realistic and Time-Consuming Tasks?
Ori Yoran, Samuel Joseph Amouyal, Chaitanya Malaviya +3
Language agents, built on top of language models (LMs), are systems that can interact with complex environments, such as the open web. In this work, we examine whether such agents…
From Loops to Oops: Fallback Behaviors of Language Models Under Uncertainty
Maor Ivgi, Ori Yoran, Jonathan Berant +1
Large language models (LLMs) often exhibit undesirable behaviors, such as hallucinations and sequence repetitions. We propose to view these behaviors as fallbacks that models exhib…
DOLOMITES: Domain-Specific Long-Form Methodical Tasks
Chaitanya Malaviya, Priyanka Agrawal, Kuzman Ganchev +7
Experts in various fields routinely perform methodical writing tasks to plan, organize, and report their work. From a clinician writing a differential diagnosis for a patient, to a…
In-Context Learning with Long-Context Models: An In-Depth Exploration
Amanda Bertsch, Maor Ivgi, Emily Xiao +4
As model context lengths continue to increase, the number of demonstrations that can be provided in-context approaches the size of entire training datasets. We study the behavior o…
Large Language Models for Psycholinguistic Plausibility Pretesting
Samuel Joseph Amouyal, Aya Meltzer-Asscher, Jonathan Berant
In psycholinguistics, the creation of controlled materials is crucial to ensure that research outcomes are solely attributed to the intended manipulations and not influenced by ext…
Transforming and Combining Rewards for Aligning Large Language Models
Zihao Wang, Chirag Nagpal, Jonathan Berant +4
A common approach for aligning language models to human preferences is to first learn a reward model from preference data, and then use this reward model to update the language mod…