14 citations · 23 across the 3 of their papers we have counts for
3 papers
cs.CL2024
Increasing the Difficulty of Automatically Generated Questions via Reinforcement Learning with Synthetic Preference
William Thorne, Ambrose Robinson, Bohua Peng +2
As the cultural heritage sector increasingly adopts technologies like Retrieval-Augmented Generation (RAG) to provide more personalised search experiences and enable conversations…
cs.CL2023★ 9 cited
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…
cs.CL2023★ 14 cited
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…