30 citations · 41 across the 8 of their papers we have counts for
8 papers · 1 filter
FollowBench: A Multi-level Fine-grained Constraints Following Benchmark for Large Language Models
Yuxin Jiang, Yufei Wang, Xingshan Zeng +7
The ability to follow instructions is crucial for Large Language Models (LLMs) to handle various real-world applications. Existing benchmarks primarily focus on evaluating pure res…
Exploiting Correlations Between Contexts and Definitions with Multiple Definition Modeling
Linhan Zhang, Qian Chen, Wen Wang +4
Definition modeling is an important task in advanced natural language applications such as understanding and conversation. Since its introduction, it focus on generating one defini…
Lion: Adversarial Distillation of Proprietary Large Language Models
Yuxin Jiang, Chunkit Chan, Mingyang Chen +1
The practice of transferring knowledge from a sophisticated, proprietary large language model (LLM) to a compact, open-source LLM has garnered considerable attention. Previous work…
ChatGPT Evaluation on Sentence Level Relations: A Focus on Temporal, Causal, and Discourse Relations
Chunkit Chan, Jiayang Cheng, Weiqi Wang +4
This paper aims to quantitatively evaluate the performance of ChatGPT, an interactive large language model, on inter-sentential relations such as temporal relations, causal relatio…
Weighted Sampling for Masked Language Modeling
Linhan Zhang, Qian Chen, Wen Wang +5
Masked Language Modeling (MLM) is widely used to pretrain language models. The standard random masking strategy in MLM causes the pre-trained language models (PLMs) to be biased to…
Global and Local Hierarchy-aware Contrastive Framework for Implicit Discourse Relation Recognition
Yuxin Jiang, Linhan Zhang, Wei Wang
Due to the absence of explicit connectives, implicit discourse relation recognition (IDRR) remains a challenging task in discourse analysis. The critical step for IDRR is to learn…
Improved Universal Sentence Embeddings with Prompt-based Contrastive Learning and Energy-based Learning
Yuxin Jiang, Linhan Zhang, Wei Wang
Contrastive learning has been demonstrated to be effective in enhancing pre-trained language models (PLMs) to derive superior universal sentence embeddings. However, existing contr…
XRJL-HKUST at SemEval-2021 Task 4: WordNet-Enhanced Dual Multi-head Co-Attention for Reading Comprehension of Abstract Meaning
Yuxin Jiang, Ziyi Shou, Qijun Wang +2
This paper presents our submitted system to SemEval 2021 Task 4: Reading Comprehension of Abstract Meaning. Our system uses a large pre-trained language model as the encoder and an…