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pro vyhledávání: '"Clarke , Christopher"'
Deciding which large language model (LLM) to use is a complex challenge. Pairwise ranking has emerged as a new method for evaluating human preferences for LLMs. This approach entails humans evaluating pairs of model outputs based on a predefined crit
Externí odkaz:
http://arxiv.org/abs/2411.14483
The recent emergence of Large Language Models (LLMs) has heralded a new era of human-AI interaction. These sophisticated models, exemplified by Chat-GPT and its successors, have exhibited remarkable capabilities in language understanding. However, as
Externí odkaz:
http://arxiv.org/abs/2407.18078
Autor:
Mars, Jason, Kang, Yiping, Dantanarayana, Jayanaka L., Irugalbandara, Chandra, Sivasothynathan, Kugesan, Clarke, Christopher, Li, Baichuan, Tang, Lingjia
Programming with Generative AI (GenAI) models, which frequently involves using large language models (LLMs) to accomplish specific functionalities, has experienced significant growth in adoption. However, it remains a complex process, as developers o
Externí odkaz:
http://arxiv.org/abs/2405.08965
While major languages often enjoy substantial attention and resources, the linguistic diversity across the globe encompasses a multitude of smaller, indigenous, and regional languages that lack the same level of computational support. One such region
Externí odkaz:
http://arxiv.org/abs/2405.03832
Autor:
Clarke, Christopher, Hall, Matthew, Mittal, Gaurav, Yu, Ye, Sajeev, Sandra, Mars, Jason, Chen, Mei
Classic approaches to content moderation typically apply a rule-based heuristic approach to flag content. While rules are easily customizable and intuitive for humans to interpret, they are inherently fragile and lack the flexibility or robustness ne
Externí odkaz:
http://arxiv.org/abs/2307.12935
Autor:
Clarke, Christopher, Heng, Yuzhao, Kang, Yiping, Flautner, Krisztian, Tang, Lingjia, Mars, Jason
Conventional approaches to text classification typically assume the existence of a fixed set of predefined labels to which a given text can be classified. However, in real-world applications, there exists an infinite label space for describing a give
Externí odkaz:
http://arxiv.org/abs/2305.16521
Autor:
Clarke, Christopher Johann
Music Representing Corpus Virtual (MRCV) is an open source software suite designed to explore the capabilities of Artificial Intelligence (AI) and Machine Learning (ML) in Music Generation, Sound Design, and Virtual Instrument Creation (MGSDIC). The
Externí odkaz:
http://arxiv.org/abs/2305.14948