3 citations · 3 across the 3 of their papers we have counts for
4 papers
The Hard Part Comes After Search: Benchmarking Web Agents on Synthesizing, Organizing, and Displaying Knowledge
Alexander Gill, Md Farhan Ishmam, Xuyen Nguyen +6
Existing computer-use agent benchmarks do not fully evaluate agents acting as assistants. A useful assistant retrieves information across complex, multi-step workflows, synthesizes…
Teaching People LLM's Errors and Getting it Right
Nathan Stringham, Fateme Hashemi Chaleshtori, Xinyuan Yan +3
People use large language models (LLMs) when they should not. This is partly because they see LLMs compose poems and answer intricate questions, so they understandably, but incorre…
Chain-of-Thought Unfaithfulness as Disguised Accuracy
Oliver Bentham, Nathan Stringham, Ana Marasović
Understanding the extent to which Chain-of-Thought (CoT) generations align with a large language model's (LLM) internal computations is critical for deciding whether to trust an LL…
Whispers of Doubt Amidst Echoes of Triumph in NLP Robustness
Ashim Gupta, Rishanth Rajendhran, Nathan Stringham +2
Do larger and more performant models resolve NLP's longstanding robustness issues? We investigate this question using over 20 models of different sizes spanning different architect…