14 citations · 19 across the 4 of their papers we have counts for
7 papers · 1 filter
StructFlowBench: A Structured Flow Benchmark for Multi-turn Instruction Following
Jinnan Li, Jinzhe Li, Yue Wang +2
Multi-turn instruction following capability constitutes a core competency of large language models (LLMs) in real-world applications. Existing evaluation benchmarks predominantly f…
NegativePrompt: Leveraging Psychology for Large Language Models Enhancement via Negative Emotional Stimuli
Xu Wang, Cheng Li, Yi Chang +2
Large Language Models (LLMs) have become integral to a wide spectrum of applications, ranging from traditional computing tasks to advanced artificial intelligence (AI) applications…
Language Models can Evaluate Themselves via Probability Discrepancy
Tingyu Xia, Bowen Yu, Yuan Wu +2
In this paper, we initiate our discussion by demonstrating how Large Language Models (LLMs), when tasked with responding to queries, display a more even probability distribution in…
Margin Discrepancy-based Adversarial Training for Multi-Domain Text Classification
Yuan Wu
Multi-domain text classification (MDTC) endeavors to harness available resources from correlated domains to enhance the classification accuracy of the target domain. Presently, mos…
Regularized Conditional Alignment for Multi-Domain Text Classification
Juntao Hu, Yuan Wu
The most successful multi-domain text classification (MDTC) approaches employ the shared-private paradigm to facilitate the enhancement of domain-invariant features through domain-…
A Survey on Evaluation of Large Language Models
Yupeng Chang, Xu Wang, Jindong Wang +13
Large language models (LLMs) are gaining increasing popularity in both academia and industry, owing to their unprecedented performance in various applications. As LLMs continue to…