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pro vyhledávání: '"Chen, Lihu"'
Autor:
Chen, Lihu, Varoquaux, Gaël
Large Language Models (LLMs) have made significant progress in advancing artificial general intelligence (AGI), leading to the development of increasingly large models such as GPT-4 and LLaMA-405B. However, scaling up model sizes results in exponenti
Externí odkaz:
http://arxiv.org/abs/2409.06857
Large Language Models (LLMs) possess vast amounts of knowledge within their parameters, prompting research into methods for locating and editing this knowledge. Previous work has largely focused on locating entity-related (often single-token) facts i
Externí odkaz:
http://arxiv.org/abs/2406.10868
Large Language Models (LLMs), including ChatGPT and LLaMA, are susceptible to generating hallucinated answers in a confident tone. While efforts to elicit and calibrate confidence scores have proven useful, recent findings show that controlling uncer
Externí odkaz:
http://arxiv.org/abs/2402.04957
Phrase representations play an important role in data science and natural language processing, benefiting various tasks like Entity Alignment, Record Linkage, Fuzzy Joins, and Paraphrase Classification. The current state-of-the-art method involves fi
Externí odkaz:
http://arxiv.org/abs/2401.10407
Positional Encodings (PEs) are used to inject word-order information into transformer-based language models. While they can significantly enhance the quality of sentence representations, their specific contribution to language models is not fully und
Externí odkaz:
http://arxiv.org/abs/2310.12864
Autor:
Suchanek, Fabian, Alam, Mehwish, Bonald, Thomas, Chen, Lihu, Paris, Pierre-Henri, Soria, Jules
Knowledge Bases (KBs) find applications in many knowledge-intensive tasks and, most notably, in information retrieval. Wikidata is one of the largest public general-purpose KBs. Yet, its collaborative nature has led to a convoluted schema and taxonom
Externí odkaz:
http://arxiv.org/abs/2308.11884
Despite their impressive scale, knowledge bases (KBs), such as Wikidata, still contain significant gaps. Language models (LMs) have been proposed as a source for filling these gaps. However, prior works have focused on prominent entities with rich co
Externí odkaz:
http://arxiv.org/abs/2306.17472
Acronym Disambiguation (AD) is crucial for natural language understanding on various sources, including biomedical reports, scientific papers, and search engine queries. However, existing acronym disambiguation benchmarks and tools are limited to spe
Externí odkaz:
http://arxiv.org/abs/2302.01860
Autor:
Liu, Qingquan1 (AUTHOR) liuqq_wy2022@163.com, Chen, Lihu1 (AUTHOR) chenlihu05@nudt.edu.cn, Li, Songting1 (AUTHOR), Xiang, Yiran1 (AUTHOR)
Publikováno v:
Sensors (14248220). Sep2024, Vol. 24 Issue 18, p6082. 14p.
State-of-the-art NLP systems represent inputs with word embeddings, but these are brittle when faced with Out-of-Vocabulary (OOV) words. To address this issue, we follow the principle of mimick-like models to generate vectors for unseen words, by lea
Externí odkaz:
http://arxiv.org/abs/2203.07860