11 citations · 49 across the 20 of their papers we have counts for
20 papers
RIGOURATE: Quantifying Scientific Exaggeration with Evidence-Aligned Claim Evaluation
Joseph James, Chenghao Xiao, Yucheng Li +2
Scientific rigour tends to be sidelined in favour of bold statements, leading authors to overstate claims beyond what their results support. We present RIGOURATE, a two-stage multi…
LongEval: A Comprehensive Analysis of Long-Text Generation Through a Plan-based Paradigm
Siwei Wu, Yizhi Li, Xingwei Qu +7
Large Language Models (LLMs) have achieved remarkable success in various natural language processing tasks, yet their ability to generate long-form content remains poorly understoo…
SCBench: A KV Cache-Centric Analysis of Long-Context Methods
Yucheng Li, Huiqiang Jiang, Qianhui Wu +8
Long-context LLMs have enabled numerous downstream applications but also introduced significant challenges related to computational and memory efficiency. To address these challeng…
On the Rigour of Scientific Writing: Criteria, Analysis, and Insights
Joseph James, Chenghao Xiao, Yucheng Li +1
Rigour is crucial for scientific research as it ensures the reproducibility and validity of results and findings. Despite its importance, little work exists on modelling rigour com…
With Ears to See and Eyes to Hear: Sound Symbolism Experiments with Multimodal Large Language Models
Tyler Loakman, Yucheng Li, Chenghua Lin
Recently, Large Language Models (LLMs) and Vision Language Models (VLMs) have demonstrated aptitude as potential substitutes for human participants in experiments testing psycholin…
Data Contamination Report from the 2024 CONDA Shared Task
Oscar Sainz, Iker García-Ferrero, Alon Jacovi +25
The 1st Workshop on Data Contamination (CONDA 2024) focuses on all relevant aspects of data contamination in natural language processing, where data contamination is understood as…
MInference 1.0: Accelerating Pre-filling for Long-Context LLMs via Dynamic Sparse Attention
Huiqiang Jiang, Yucheng Li, Chengruidong Zhang +9
The computational challenges of Large Language Model (LLM) inference remain a significant barrier to their widespread deployment, especially as prompt lengths continue to increase.…
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…
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…
Nominal Metaphor Generation with Multitask Learning
Yucheng Li, Chenghua Lin, Frank Geurin
Metaphor generation is a challenging task which can impact many downstream tasks such as improving user satisfaction with dialogue systems and story generation. This paper tackles…
Recent Advances in Neural Text Generation: A Task-Agnostic Survey
Chen Tang, Frank Guerin, Chenghua Lin
In recent years, considerable research has been dedicated to the application of neural models in the field of natural language generation (NLG). The primary objective is to generat…