5 citations · 15 across the 14 of their papers we have counts for
10 papers · 1 filter
DualCoTs: Dual Chain-of-Thoughts Prompting for Sentiment Lexicon Expansion of Idioms
Fuqiang Niu, Minghuan Tan, Bowen Zhang +2
Idioms represent a ubiquitous vehicle for conveying sentiments in the realm of everyday discourse, rendering the nuanced analysis of idiom sentiment crucial for a comprehensive und…
Training on the Benchmark Is Not All You Need
Shiwen Ni, Xiangtao Kong, Chengming Li +4
The success of Large Language Models (LLMs) relies heavily on the huge amount of pre-training data learned in the pre-training phase. The opacity of the pre-training process and th…
Improving In-Context Learning with Prediction Feedback for Sentiment Analysis
Hongling Xu, Qianlong Wang, Yice Zhang +4
Large language models (LLMs) have achieved promising results in sentiment analysis through the in-context learning (ICL) paradigm. However, their ability to distinguish subtle sent…
NUMCoT: Numerals and Units of Measurement in Chain-of-Thought Reasoning using Large Language Models
Ancheng Xu, Minghuan Tan, Lei Wang +2
Numeral systems and units of measurement are two conjoined topics in activities of human beings and have mutual effects with the languages expressing them. Currently, the evaluatio…
II-Bench: An Image Implication Understanding Benchmark for Multimodal Large Language Models
Ziqiang Liu, Feiteng Fang, Xi Feng +23
The rapid advancements in the development of multimodal large language models (MLLMs) have consistently led to new breakthroughs on various benchmarks. In response, numerous challe…
Enhancing Noise Robustness of Retrieval-Augmented Language Models with Adaptive Adversarial Training
Feiteng Fang, Yuelin Bai, Shiwen Ni +3
Large Language Models (LLMs) exhibit substantial capabilities yet encounter challenges, including hallucination, outdated knowledge, and untraceable reasoning processes. Retrieval-…
CLHA: A Simple yet Effective Contrastive Learning Framework for Human Alignment
Feiteng Fang, Liang Zhu, Min Yang +6
Reinforcement learning from human feedback (RLHF) is a crucial technique in aligning large language models (LLMs) with human preferences, ensuring these LLMs behave in beneficial a…
MoZIP: A Multilingual Benchmark to Evaluate Large Language Models in Intellectual Property
Shiwen Ni, Minghuan Tan, Yuelin Bai +9
Large language models (LLMs) have demonstrated impressive performance in various natural language processing (NLP) tasks. However, there is limited understanding of how well LLMs p…
Layer-wise Regularized Dropout for Neural Language Models
Shiwen Ni, Min Yang, Ruifeng Xu +2
Among the various pre-trained neural language models that are popular today, dropout is already an indispensable regularization technique. To solve the inconsistency between traini…
E-EVAL: A Comprehensive Chinese K-12 Education Evaluation Benchmark for Large Language Models
Jinchang Hou, Chang Ao, Haihong Wu +8
With the accelerating development of Large Language Models (LLMs), many LLMs are beginning to be used in the Chinese K-12 education domain. The integration of LLMs and education is…