3 citations · 3 across the 24 of their papers we have counts for
9 papers · 1 filter
ELBench: A Multi-Dimensional Benchmark for Education-Facing Large Language Models
Yilin Jiang, Xiaorong Zhu, Fei Tan +9
Large language models are increasingly deployed in education as tutors, teaching assistants, and content generators. These roles place demands that ordinary question answering does…
LatentRevise: Learning from Zero-Hit Reasoning
Yiqiu Guo, Xueting Han, Qi Jia +2
Reinforcement learning with verifiable rewards (RLVR) is bottlenecked by hard prompts on which correct trajectories have low probability, so sampling misses them within a practical…
UniDial-EvalKit: A Unified Toolkit for Evaluating Multi-Faceted Conversational Abilities
Qi Jia, Haodong Zhao, Dun Pei +7
Benchmarking large language models (LLMs) and agents in multi-turn interactive scenarios is essential for understanding their practical capabilities. However, existing evaluation p…
EvolMem: A Cognitive-Driven Benchmark for Multi-Session Dialogue Memory
Ye Shen, Dun Pei, Yiqiu Guo +6
Despite recent advances in understanding and leveraging long-range conversational memory, existing benchmarks still lack systematic evaluation of large language models(LLMs) across…
Evaluating from Benign to Dynamic Adversarial: A Squid Game for Large Language Models
Zijian Chen, Wenjun Zhang, Guangtao Zhai
The potential data contamination issue in contemporary large language models (LLMs) benchmarks presents a fundamental challenge to establishing trustworthy evaluation frameworks. M…
One Battle After Another: Probing LLMs' Limits on Multi-Turn Instruction Following with a Benchmark Evolving Framework
Qi Jia, Ye Shen, Xiujie Song +5
Evaluating LLMs' instruction-following ability in multi-topic dialogues is essential yet challenging. Existing benchmarks are limited to a fixed number of turns, susceptible to sat…
Sycophancy under Pressure: Evaluating and Mitigating Sycophantic Bias via Adversarial Dialogues in Scientific QA
Kaiwei Zhang, Qi Jia, Zijian Chen +5
Large language models (LLMs), while increasingly used in domains requiring factual rigor, often display a troubling behavior: sycophancy, the tendency to align with user beliefs re…
User-centric Subjective Leaderboard by Customizable Reward Modeling
Qi Jia, Xiujie Song, Zicheng Zhang +4
Existing benchmarks for large language models (LLMs) predominantely focus on assessing their capabilities through verifiable tasks. Such objective and static benchmarks offer limit…
The Ever-Evolving Science Exam
Junying Wang, Zicheng Zhang, Yijin Guo +9
As foundation models grow rapidly in capability and deployment, evaluating their scientific understanding becomes increasingly critical. Existing science benchmarks have made progr…