2 citations · 3 across the 4 of their papers we have counts for
4 papers
AntiLeakBench: Preventing Data Contamination by Automatically Constructing Benchmarks with Updated Real-World Knowledge
Xiaobao Wu, Liangming Pan, Yuxi Xie +7
Data contamination hinders fair LLM evaluation by introducing test data into newer models' training sets. Existing studies solve this challenge by updating benchmarks with newly co…
RuleArena: A Benchmark for Rule-Guided Reasoning with LLMs in Real-World Scenarios
Ruiwen Zhou, Wenyue Hua, Liangming Pan +4
This paper introduces RuleArena, a novel and challenging benchmark designed to evaluate the ability of large language models (LLMs) to follow complex, real-world rules in reasoning…
COrAL: Order-Agnostic Language Modeling for Efficient Iterative Refinement
Yuxi Xie, Anirudh Goyal, Xiaobao Wu +5
Iterative refinement has emerged as an effective paradigm for enhancing the capabilities of large language models (LLMs) on complex tasks. However, existing approaches typically im…
FASTopic: Pretrained Transformer is a Fast, Adaptive, Stable, and Transferable Topic Model
Xiaobao Wu, Thong Nguyen, Delvin Ce Zhang +2
Topic models have been evolving rapidly over the years, from conventional to recent neural models. However, existing topic models generally struggle with either effectiveness, effi…