1 citations · 2 across the 8 of their papers we have counts for
19 papers
Uncertainty-based Debiasing and Unlearning for Decontamination
Guangzhi Sun, Xiao Zhan, Mark Gales
Benchmark-based evaluation is the dominant paradigm for assessing large language model (LLM) capabilities, yet data contamination inflates reported performance and undermines fair…
SkillAggregation: Reference-free LLM-Dependent Aggregation
Guangzhi Sun, Anmol Kagrecha, Potsawee Manakul +2
Large Language Models (LLMs) are increasingly used to assess NLP tasks due to their ability to generate human-like judgments. Single LLMs were used initially, however, recent work…
Finetuning LLMs for Comparative Assessment Tasks
Vatsal Raina, Adian Liusie, Mark Gales
Automated assessment in natural language generation is a challenging task. Instruction-tuned large language models (LLMs) have shown promise in reference-free evaluation, particula…
Controlling Whisper: Universal Acoustic Adversarial Attacks to Control Speech Foundation Models
Vyas Raina, Mark Gales
Speech enabled foundation models, either in the form of flexible speech recognition based systems or audio-prompted large language models (LLMs), are becoming increasingly popular.…
Cross-Lingual Transfer Learning for Speech Translation
Rao Ma, Mengjie Qian, Yassir Fathullah +3
There has been increasing interest in building multilingual foundation models for NLP and speech research. This paper examines how to expand the speech translation capability of th…
CrossCheckGPT: Universal Hallucination Ranking for Multimodal Foundation Models
Guangzhi Sun, Potsawee Manakul, Adian Liusie +4
Multimodal foundation models are prone to hallucination, generating outputs that either contradict the input or are not grounded by factual information. Given the diversity in arch…