11 citations · 49 across the 20 of their papers we have counts for
9 papers · 1 filter
LatestEval: Addressing Data Contamination in Language Model Evaluation through Dynamic and Time-Sensitive Test Construction
Yucheng Li, Frank Guerin, Chenghua Lin
Data contamination in evaluation is getting increasingly prevalent with the emergence of language models pre-trained on super large, automatically crawled corpora. This problem lea…
Compressing Context to Enhance Inference Efficiency of Large Language Models
Yucheng Li, Bo Dong, Chenghua Lin +1
Large language models (LLMs) achieved remarkable performance across various tasks. However, they face challenges in managing long documents and extended conversations, due to signi…
An Open Source Data Contamination Report for Large Language Models
Yucheng Li, Frank Guerin, Chenghua Lin
Data contamination in model evaluation has become increasingly prevalent with the growing popularity of large language models. It allows models to "cheat" via memorisation instead…
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…
FrameBERT: Conceptual Metaphor Detection with Frame Embedding Learning
Yucheng Li, Shun Wang, Chenghua Lin +2
In this paper, we propose FrameBERT, a RoBERTa-based model that can explicitly learn and incorporate FrameNet Embeddings for concept-level metaphor detection. FrameBERT not only ac…
The Secret of Metaphor on Expressing Stronger Emotion
Yucheng Li, Frank Guerin, Chenghua Lin
Metaphors are proven to have stronger emotional impact than literal expressions. Although this conclusion is shown to be promising in benefiting various NLP applications, the reaso…