23 citations · 56 across the 9 of their papers we have counts for
6 papers · 1 filter
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…
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…