2 citations · 3 across the 17 of their papers we have counts for
7 papers · 1 filter
Learning Checklists to Reduce Lottery Effects in Scientific Peer Review
Xuanyou Liu, Ziyang Guo, Jason Hartline +1
Reviewer lottery error describes error due to a small set of peer reviews attending to only some of the issues with a paper that a larger pool of eligible reviewers would raise. We…
Diagnosing and Improving Probabilistic Reasoning in Large Language Models
Huaman Sun, Dingcheng Wang, Jason Hartline +1
Large language models (LLMs) are increasingly proposed as decision assistants who must reason probabilistically from available evidence under explicit decision costs. We propose a…
Truthful Calibration Measures for Sequential Prediction
Anagha Gokul, Jason Hartline, Lunjia Hu +2
Calibration requires probabilistic reports to be conditionally unbiased and reliably interpretable as probabilities. A calibration measure assigns numerical error to miscalibrated…
Sample Complexity of Peer Prediction
Abdellah Aznag, Robin Bowers, Rachel Cummings +3
Peer prediction seeks to incentivize agents to truthfully report an observed signal by rewarding joint sets of reports without observing a ground truth. Following the generalizatio…
Scoring Rules! Statistical and Strategic Alignment for Text Evaluation Metrics
Shengwei Xu, Yuxuan Lu, Yifan Wu +2
Reference-based text evaluation metrics, which are widely used to assess natural language generation systems, score a candidate response by comparing it with a reference response.…
ComplLLM: Fine-tuning LLMs to Discover Complementary Signals for Decision-making
Ziyang Guo, Yifan Wu, Jason Hartline +2
Multi-agent decision pipelines can outperform single agent workflows when complementarity holds, i.e., different agents bring unique information to the table to inform a final deci…
Clarification of `Algorithmic Collusion without Threats'
Jason Hartline
This brief note clarifies that the scenario described in Arunachaleswaran et al. (2025) -- titled `Algorithmic Collusion without Threats' -- is not one of collusion, but one where…