1 citations · 1 across the 7 of their papers we have counts for
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Beg to Differ: Understanding Reasoning-Answer Misalignment Across Languages
Anaelia Ovalle, Candace Ross, Sebastian Ruder +4
Large language models demonstrate strong reasoning capabilities through chain-of-thought prompting, but whether this reasoning quality transfers across languages remains underexplo…
OpenApps: Simulating Environment Variations to Measure UI-Agent Reliability
Karen Ullrich, Jingtong Su, Claudia Shi +7
Reliability is key to realizing the promise of autonomous UI-Agents, multimodal agents that directly interact with apps in the same manner as humans, as users must be able to trust…
What's in Common? Multimodal Models Hallucinate When Reasoning Across Scenes
Candace Ross, Florian Bordes, Adina Williams +2
Multimodal language models possess a remarkable ability to handle an open-vocabulary's worth of objects. Yet the best models still suffer from hallucinations when reasoning about s…
LLM Knowledge is Brittle: Truthfulness Representations Rely on Superficial Resemblance
Patrick Haller, Mark Ibrahim, Polina Kirichenko +2
For Large Language Models (LLMs) to be reliable, they must learn robust knowledge that can be generally applied in diverse settings -- often unlike those seen during training. Yet,…
A Single Character can Make or Break Your LLM Evals
Jingtong Su, Jianyu Zhang, Karen Ullrich +2
Common Large Language model (LLM) evaluations rely on demonstration examples to steer models' responses to the desired style. While the number of examples used has been studied and…