23 citations · 56 across the 9 of their papers we have counts for
10 papers
ESG-Bench: Benchmarking Long-Context ESG Reports for Hallucination Mitigation
Siqi Sun, Ben Peng Wu, Mali Jin +3
As corporate responsibility increasingly incorporates environmental, social, and governance (ESG) criteria, ESG reporting is becoming a legal requirement in many regions and a key…
Dense SAE Latents Are Features, Not Bugs
Xiaoqing Sun, Alessandro Stolfo, Joshua Engels +4
Sparse autoencoders (SAEs) are designed to extract interpretable features from language models by enforcing a sparsity constraint. Ideally, training an SAE would yield latents that…
GOD model: Privacy Preserved AI School for Personal Assistant
PIN AI Team, Bill Sun, Gavin Guo +7
Personal AI assistants (e.g., Apple Intelligence, Meta AI) offer proactive recommendations that simplify everyday tasks, but their reliance on sensitive user data raises concerns a…
Efficient Annotator Reliability Assessment and Sample Weighting for Knowledge-Based Misinformation Detection on Social Media
Owen Cook, Charlie Grimshaw, Ben Wu +6
Misinformation spreads rapidly on social media, confusing the truth and targeting potentially vulnerable people. To effectively mitigate the negative impact of misinformation, it m…
Confidence Regulation Neurons in Language Models
Alessandro Stolfo, Ben Wu, Wes Gurnee +4
Despite their widespread use, the mechanisms by which large language models (LLMs) represent and regulate uncertainty in next-token predictions remain largely unexplored. This stud…
Don't Waste a Single Annotation: Improving Single-Label Classifiers Through Soft Labels
Ben Wu, Yue Li, Yida Mu +3
In this paper, we address the limitations of the common data annotation and training methods for objective single-label classification tasks. Typically, when annotating such tasks…
Bio-SIEVE: Exploring Instruction Tuning Large Language Models for Systematic Review Automation
Ambrose Robinson, William Thorne, Ben P. Wu +4
Medical systematic reviews can be very costly and resource intensive. We explore how Large Language Models (LLMs) can support and be trained to perform literature screening when pr…
Comparison between parameter-efficient techniques and full fine-tuning: A case study on multilingual news article classification
Olesya Razuvayevskaya, Ben Wu, Joao A. Leite +5
Adapters and Low-Rank Adaptation (LoRA) are parameter-efficient fine-tuning techniques designed to make the training of language models more efficient. Previous results demonstrate…
Navigating Prompt Complexity for Zero-Shot Classification: A Study of Large Language Models in Computational Social Science
Yida Mu, Ben P. Wu, William Thorne +5
Instruction-tuned Large Language Models (LLMs) have exhibited impressive language understanding and the capacity to generate responses that follow specific prompts. However, due to…
SheffieldVeraAI at SemEval-2023 Task 3: Mono and multilingual approaches for news genre, topic and persuasion technique classification
Ben Wu, Olesya Razuvayevskaya, Freddy Heppell +4
This paper describes our approach for SemEval-2023 Task 3: Detecting the category, the framing, and the persuasion techniques in online news in a multi-lingual setup. For Subtask 1…