20 citations · 35 across the 9 of their papers we have counts for
9 papers
Uncertainty Quantification for LLM Agents: A Taxonomy, an Evaluation Protocol, and an Empirical Study
Moule Lin, Qizhen Lan, Shuhao Guan +4
Large language models (LLMs) are no longer deployed only for single-turn conversation but increasingly act as agents that plan, call tools, retrieve evidence, maintain memory, and…
LiveFact: A Dynamic, Time-Aware Benchmark for LLM-Driven Fake News Detection
Cheng Xu, Changhong Jin, Yingjie Niu +5
The rapid development of Large Language Models (LLMs) has transformed fake news detection and fact-checking tasks from simple classification to complex reasoning. However, evaluati…
Bayesian-LoRA: Probabilistic Low-Rank Adaptation of Large Language Models
Moule Lin, Shuhao Guan, Andrea Patane +2
Large Language Models usually put more emphasis on accuracy and therefore, will guess even when not certain about the prediction, which is especially severe when fine-tuned on smal…
Flow-Induced Diagonal Gaussian Processes
Moule Lin, Andrea Patane, Weipeng Jing +2
We present Flow-Induced Diagonal Gaussian Processes (FiD-GP), a compression framework that incorporates a compact inducing weight matrix to project a neural network's weight uncert…
DCR: Quantifying Data Contamination in LLMs Evaluation
Cheng Xu, Nan Yan, Shuhao Guan +4
The rapid advancement of large language models (LLMs) has heightened concerns about benchmark data contamination (BDC), where models inadvertently memorize evaluation data during t…
PreP-OCR: A Complete Pipeline for Document Image Restoration and Enhanced OCR Accuracy
Shuhao Guan, Moule Lin, Cheng Xu +5
This paper introduces PreP-OCR, a two-stage pipeline that combines document image restoration with semantic-aware post-OCR correction to enhance both visual clarity and textual con…
Stochastic Weight Sharing for Bayesian Neural Networks
Moule Lin, Shuhao Guan, Weipeng Jing +2
While offering a principled framework for uncertainty quantification in deep learning, the employment of Bayesian Neural Networks (BNNs) is still constrained by their increased com…
Advancing Post-OCR Correction: A Comparative Study of Synthetic Data
Shuhao Guan, Derek Greene
This paper explores the application of synthetic data in the post-OCR domain on multiple fronts by conducting experiments to assess the impact of data volume, augmentation, and syn…
Benchmark Data Contamination of Large Language Models: A Survey
Cheng Xu, Shuhao Guan, Derek Greene +1
The rapid development of Large Language Models (LLMs) like GPT-4, Claude-3, and Gemini has transformed the field of natural language processing. However, it has also resulted in a…