Zobrazeno 1 - 10
of 24
pro vyhledávání: '"Manakul, Potsawee"'
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 suggests using multiple LLMs as judges yields improved performance. An
Externí odkaz:
http://arxiv.org/abs/2410.10215
Autor:
Manakul, Potsawee, Sun, Guangzhi, Sirichotedumrong, Warit, Tharnpipitchai, Kasima, Pipatanakul, Kunat
Audio language models can understand audio inputs and perform a range of audio-related tasks based on instructions, such as speech recognition and audio captioning, where the instructions are usually textual prompts. Audio language models are mostly
Externí odkaz:
http://arxiv.org/abs/2409.10999
Autor:
Sun, Guangzhi, Manakul, Potsawee, Liusie, Adian, Pipatanakul, Kunat, Zhang, Chao, Woodland, Phil, Gales, Mark
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 architectures, training data and instruction tuning techniques, there can
Externí odkaz:
http://arxiv.org/abs/2405.13684
Autor:
Pipatanakul, Kunat, Jirabovonvisut, Phatrasek, Manakul, Potsawee, Sripaisarnmongkol, Sittipong, Patomwong, Ruangsak, Chokchainant, Pathomporn, Tharnpipitchai, Kasima
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 LLMs, including data preparation, pretraining, instruction-tuning, and ev
Externí odkaz:
http://arxiv.org/abs/2312.13951
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 performance, with semantically equivalent settings often showing notable perfor
Externí odkaz:
http://arxiv.org/abs/2309.04992
Current developments in large language models (LLMs) have enabled impressive zero-shot capabilities across various natural language tasks. An interesting application of these systems is in the automated assessment of natural language generation (NLG)
Externí odkaz:
http://arxiv.org/abs/2307.07889
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 the decoding results of a target ASR system. This approach can be com
Externí odkaz:
http://arxiv.org/abs/2307.04172
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 BioNLP Workshop 2023, we demonstrate that Clinical-T5 fine-tuned to 765 med
Externí odkaz:
http://arxiv.org/abs/2306.05317
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 facts and make non-factual statements which can undermine trust in their
Externí odkaz:
http://arxiv.org/abs/2303.08896
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 source. Hence, an important component of assessing the quality of summari
Externí odkaz:
http://arxiv.org/abs/2301.12307