Results 11 to 20 of about 21,073,882 (364)
Is ChatGPT a General-Purpose Natural Language Processing Task Solver? [PDF]
Conference on Empirical Methods in Natural Language Processing, 2023 Spurred by advancements in scale, large language models (LLMs) have demonstrated the ability to perform a variety of natural language processing (NLP) tasks zero-shot -- i.e., without adaptation on downstream data.
Chengwei Qin +5 more
openalex +3 more sources
Survey of Hallucination in Natural Language Generation [PDF]
ACM Computing Surveys, 2022 Natural Language Generation (NLG) has improved exponentially in recent years thanks to the development of sequence-to-sequence deep learning technologies such as Transformer-based language models. This advancement has led to more fluent and coherent NLG,
Ziwei Ji +11 more
semanticscholar +1 more source
CodeBERT: A Pre-Trained Model for Programming and Natural Languages [PDF]
Findings, 2020 We present CodeBERT, a bimodal pre-trained model for programming language (PL) and natural language (NL). CodeBERT learns general-purpose representations that support downstream NL-PL applications such as natural language code search, code documentation ...
Zhangyin Feng +10 more
semanticscholar +1 more source
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding [PDF]
BlackboxNLP@EMNLP, 2018 Human ability to understand language is general, flexible, and robust. In contrast, most NLU models above the word level are designed for a specific task and struggle with out-of-domain data.
Alex Wang +5 more
semanticscholar +1 more source
A large annotated corpus for learning natural language inference [PDF]
Conference on Empirical Methods in Natural Language Processing, 2015 Understanding entailment and contradiction is fundamental to understanding natural language, and inference about entailment and contradiction is a valuable testing ground for the development of semantic representations. However, machine learning research
Samuel R. Bowman +3 more
semanticscholar +1 more source
Natural products in drug discovery: advances and opportunities
Nature reviews. Drug discovery, 2021 Natural products and their structural analogues have historically made a major contribution to pharmacotherapy, especially for cancer and infectious diseases.
A. Atanasov +73 more
semanticscholar +1 more source
Natural Products as Sources of New Drugs over the Nearly Four Decades from 01/1981 to 09/2019.
Journal of Natural Products, 2020 This review is an updated and expanded version of the five prior reviews that were published in this journal in 1997, 2003, 2007, 2012, and 2016. For all approved therapeutic agents, the time frame has been extended to cover the almost 39 years from the ...
D. Newman, G. Cragg
semanticscholar +1 more source
Natural Questions: A Benchmark for Question Answering Research
Transactions of the Association for Computational Linguistics, 2019 We present the Natural Questions corpus, a question answering data set. Questions consist of real anonymized, aggregated queries issued to the Google search engine.
T. Kwiatkowski +17 more
semanticscholar +1 more source
PIQA: Reasoning about Physical Commonsense in Natural Language [PDF]
AAAI Conference on Artificial Intelligence, 2019 To apply eyeshadow without a brush, should I use a cotton swab or a toothpick? Questions requiring this kind of physical commonsense pose a challenge to today's natural language understanding systems.
Yonatan Bisk +4 more
semanticscholar +1 more source
TinyBERT: Distilling BERT for Natural Language Understanding [PDF]
Findings, 2019 Language model pre-training, such as BERT, has significantly improved the performances of many natural language processing tasks. However, pre-trained language models are usually computationally expensive, so it is difficult to efficiently execute them ...
Xiaoqi Jiao +7 more
semanticscholar +1 more source