1 citations · 1 across the 6 of their papers we have counts for
7 papers
Human-LLM Deliberation as Interactive Proof: Conditions for Verifiability Without Transparency
Baotong Zhang, Dean Foster, João Sedoc
When an LLM supplies an argument a user could not produce, how can the user decide whether to accept its claim? Inspired by interactive proofs, we pair a computationally unbounded…
Epistemic Warrant for LLM Recommendations: Characterizing the Basis for Reliance When Ground Truth Is Unavailable
Shai Vardi, João Sedoc
Large language models are increasingly used to support organizational decisions, yet users often lack a principled basis for assessing whether to rely on a specific recommendation.…
Trust but Verify: Prover-Verifier Deliberation for Selective LLM Prediction
João Sedoc, Baotong Zhang, Dean Foster
Reliably knowing when a language model is correct is almost as important as being correct. We introduce prover-verifier deliberation (PVD), an inference-time protocol grounded in i…
Conceptors for Semantic Steering
Ilias Triantafyllopoulos, Young-Min Cho, Ren Tao +6
Activation-based steering provides control of LLM behavior at inference time, but the dominant paradigm reduces each concept to a single direction whose geometry is left largely un…
DBOT: Artificial Intelligence for Systematic Long-Term Investing
Vasant Dhar, João Sedoc
Long-term investing was previously seen as requiring human judgment. With the advent of generative artificial intelligence (AI) systems, automated systematic long-term investing is…
Reasoning and the Trusting Behavior of DeepSeek and GPT: An Experiment Revealing Hidden Fault Lines in Large Language Models
Rubing Li, João Sedoc, Arun Sundararajan
When encountering increasingly frequent performance improvements or cost reductions from a new large language model (LLM), developers of applications leveraging LLMs must decide wh…
To Err Is Human; To Annotate, SILICON? Toward Robust Reproducibility in LLM Annotation
Xiang Cheng, Raveesh Mayya, João Sedoc
Unstructured text data annotation is foundational to management research. LLMs offer a cost-effective and scalable alternative to human annotation, but they introduce a novel chall…