39 citations · 64 across the 13 of their papers we have counts for
16 papers
Unlearning vs. Obfuscation: Are We Truly Removing Knowledge?
Guangzhi Sun, Potsawee Manakul, Xiao Zhan +1
Unlearning has emerged as a critical capability for large language models (LLMs) to support data privacy, regulatory compliance, and ethical AI deployment. Recent techniques often…
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…
Enhancing Low-Resource Language and Instruction Following Capabilities of Audio Language Models
Potsawee Manakul, Guangzhi Sun, Warit Sirichotedumrong +2
Audio language models process audio inputs using textual prompts for tasks like speech recognition and audio captioning. Although built on multilingual pre-trained components, most…
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…
Typhoon: Thai Large Language Models
Kunat Pipatanakul, Phatrasek Jirabovonvisut, Potsawee Manakul +4
Typhoon is a series of Thai large language models (LLMs) developed specifically for the Thai language. This technical report presents challenges and insights in developing Thai LLM…
Mitigating Word Bias in Zero-shot Prompt-based Classifiers
Adian Liusie, Potsawee Manakul, Mark J. F. Gales
Prompt-based classifiers are an attractive approach for zero-shot classification. However, the precise choice of the prompt template and label words can largely influence performan…
LLM Comparative Assessment: Zero-shot NLG Evaluation through Pairwise Comparisons using Large Language Models
Adian Liusie, Potsawee Manakul, Mark J. F. Gales
Current developments in large language models (LLMs) have enabled impressive zero-shot capabilities across various natural language tasks. An interesting application of these syste…
Can Generative Large Language Models Perform ASR Error Correction?
Rao Ma, Mengjie Qian, Potsawee Manakul +2
ASR error correction is an interesting option for post processing speech recognition system outputs. These error correction models are usually trained in a supervised fashion using…
CUED at ProbSum 2023: Hierarchical Ensemble of Summarization Models
Potsawee Manakul, Yassir Fathullah, Adian Liusie +3
In this paper, we consider the challenge of summarizing patients' medical progress notes in a limited data setting. For the Problem List Summarization (shared task 1A) at the BioNL…
SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models
Potsawee Manakul, Adian Liusie, Mark J. F. Gales
Generative Large Language Models (LLMs) such as GPT-3 are capable of generating highly fluent responses to a wide variety of user prompts. However, LLMs are known to hallucinate fa…
MQAG: Multiple-choice Question Answering and Generation for Assessing Information Consistency in Summarization
Potsawee Manakul, Adian Liusie, Mark J. F. Gales
State-of-the-art summarization systems can generate highly fluent summaries. These summaries, however, may contain factual inconsistencies and/or information not present in the sou…
Podcast Summary Assessment: A Resource for Evaluating Summary Assessment Methods
Potsawee Manakul, Mark J. F. Gales
Automatic summary assessment is useful for both machine-generated and human-produced summaries. Automatically evaluating the summary text given the document enables, for example, s…
Sparsity and Sentence Structure in Encoder-Decoder Attention of Summarization Systems
Potsawee Manakul, Mark J. F. Gales
Transformer models have achieved state-of-the-art results in a wide range of NLP tasks including summarization. Training and inference using large transformer models can be computa…
Long-Span Summarization via Local Attention and Content Selection
Potsawee Manakul, Mark J. F. Gales
Transformer-based models have achieved state-of-the-art results in a wide range of natural language processing (NLP) tasks including document summarization. Typically these systems…
Attention Forcing for Machine Translation
Qingyun Dou, Yiting Lu, Potsawee Manakul +2
Auto-regressive sequence-to-sequence models with attention mechanisms have achieved state-of-the-art performance in various tasks including Text-To-Speech (TTS) and Neural Machine…
CUED_speech at TREC 2020 Podcast Summarisation Track
Potsawee Manakul, Mark Gales
In this paper, we describe our approach for the Podcast Summarisation challenge in TREC 2020. Given a podcast episode with its transcription, the goal is to generate a summary that…