10 citations · 23 across the 9 of their papers we have counts for
9 papers
Evaluating Large Language Models for Generalization and Robustness via Data Compression
Yucheng Li, Yunhao Guo, Frank Guerin +1
Existing methods for evaluating large language models face challenges such as data contamination, sensitivity to prompts, and the high cost of benchmark creation. To address this,…
Finding Challenging Metaphors that Confuse Pretrained Language Models
Yucheng Li, Frank Guerin, Chenghua Lin
Metaphors are considered to pose challenges for a wide spectrum of NLP tasks. This gives rise to the area of computational metaphor processing. However, it remains unclear what typ…
Estimating Contamination via Perplexity: Quantifying Memorisation in Language Model Evaluation
Yucheng Li
Data contamination in model evaluation is getting increasingly prevalent as the massive training corpora of large language models often unintentionally include benchmark samples. T…
Metaphor Detection via Explicit Basic Meanings Modelling
Yucheng Li, Shun Wang, Chenghua Lin +1
One noticeable trend in metaphor detection is the embrace of linguistic theories such as the metaphor identification procedure (MIP) for model architecture design. While MIP clearl…
Unlocking Context Constraints of LLMs: Enhancing Context Efficiency of LLMs with Self-Information-Based Content Filtering
Yucheng Li
Large language models (LLMs) have received significant attention by achieving remarkable performance across various tasks. However, their fixed context length poses challenges when…
Metaphor Detection with Effective Context Denoising
Shun Wang, Yucheng Li, Chenghua Lin +2
We propose a novel RoBERTa-based model, RoPPT, which introduces a target-oriented parse tree structure in metaphor detection. Compared to existing models, RoPPT focuses on semantic…