88 citations · 88 across the 15 of their papers we have counts for
15 papers
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.…
ShipTraj-R1: Reinforcing Ship Trajectory Prediction in Large Language Models via Group Relative Policy Optimization
Yang Zhan, Yunhao Li, Zhang Chao +2
Recent advancements in reinforcement fine-tuning have significantly improved the reasoning ability of large language models (LLMs). In particular, methods such as group relative po…
Jailbreaking LLMs via Calibration
Yuxuan Lu, Yongkang Guo, Yuqing Kong
Safety alignment in Large Language Models (LLMs) often creates a systematic discrepancy between a model's aligned output and the underlying pre-aligned data distribution. We propos…
Truthful Calibration Errors for Multi-Class Prediction
Yuxuan Lu, Yifan Wu, Jason Hartline +1
Calibrated predictions are useful because their numerical values can be interpreted as probabilities. Calibration errors are therefore widely used to evaluate, compare, and tune pr…
LLaVAShield: Safeguarding Multimodal Multi-Turn Dialogues in Vision-Language Models
Guolei Huang, Qinzhi Peng, Gan Xu +3
As Vision-Language Models (VLMs) move into interactive, multi-turn use, safety concerns intensify for multimodal multi-turn dialogue, which is characterized by concealment of malic…
Aligned Textual Scoring Rules
Yuxuan Lu, Yifan Wu, Jason Hartline +1
Scoring rules elicit probabilistic predictions from a strategic agent by scoring the prediction against a ground truth state. A scoring rule is proper if, from the agent's perspect…
Benchmarking LLMs' Judgments with No Gold Standard
Shengwei Xu, Yuxuan Lu, Grant Schoenebeck +1
We introduce the GEM (Generative Estimator for Mutual Information), an evaluation metric for assessing language generation by Large Language Models (LLMs), particularly in generati…
How Gold to Make the Golden Snitch: Designing the "Game Changer" in Esports
Zhihuan Huang, Yuxuan Lu, Yongkang Guo +1
Many battling games utilize a special item (e.g. Roshan in Defense of the Ancients 2 (DOTA 2), Baron Nashor in League of Legends (LOL), Golden Snitch in Quidditch) as a potential `…
Eliciting Informative Text Evaluations with Large Language Models
Yuxuan Lu, Shengwei Xu, Yichi Zhang +2
Peer prediction mechanisms motivate high-quality feedback with provable guarantees. However, current methods only apply to rather simple reports, like multiple-choice or scalar num…
Calibrating "Cheap Signals" in Peer Review without a Prior
Yuxuan Lu, Yuqing Kong
Peer review lies at the core of the academic process, but even well-intentioned reviewers can still provide noisy ratings. While ranking papers by average ratings may reduce noise,…
A Framework of Transaction Packaging in High-throughput Blockchains
Yuxuan Lu, Qian Qi, Xi Chen
We develop a model of coordination and allocation of decentralized multi-sided markets, in which our theoretical analysis is promisingly optimizing the decentralized transaction pa…
FileInsurer: A Scalable and Reliable Protocol for Decentralized File Storage in Blockchain
Hongyin Chen, Yuxuan Lu, Yukun Cheng
With the development of blockchain applications, the requirements for file storage in blockchain are increasing rapidly. Many protocols, including Filecoin, Arweave, and Sia, have…
Empirical Analysis of EIP-1559: Transaction Fees, Waiting Time, and Consensus Security
Yulin Liu, Yuxuan Lu, Kartik Nayak +3
A transaction fee mechanism (TFM) is an essential component of a blockchain protocol. However, a systematic evaluation of the real-world impact of TFMs is still absent. Using rich…
SURPRISE! and When to Schedule It
Zhihuan Huang, Shengwei Xu, You Shan +4
Information flow measures, over the duration of a game, the audience's belief of who will win, and thus can reflect the amount of surprise in a game. To quantify the relationship b…
Equal Affection or Random Selection: the Quality of Subjective Feedback from a Group Perspective
Jiale Chen, Yuqing Kong, Yuxuan Lu
In the setting where a group of agents is asked a single subjective multi-choice question (e.g. which one do you prefer? cat or dog?), we are interested in evaluating the quality o…