Results 11 to 20 of about 3,152,071 (98)
Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding [PDF]
Conference on Empirical Methods in Natural Language Processing, 2023 We present Video-LLaMA a multi-modal framework that empowers Large Language Models (LLMs) with the capability of understanding both visual and auditory content in the video.
Hang Zhang, Xin Li, Lidong Bing
semanticscholar +1 more source
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
semanticscholar +1 more source
SpeechGPT: Empowering Large Language Models with Intrinsic Cross-Modal Conversational Abilities [PDF]
Conference on Empirical Methods in Natural Language Processing, 2023 Multi-modal large language models are regarded as a crucial step towards Artificial General Intelligence (AGI) and have garnered significant interest with the emergence of ChatGPT.
Dong Zhang +6 more
semanticscholar +1 more source
Red Teaming Language Models with Language Models [PDF]
Conference on Empirical Methods in Natural Language Processing, 2022 Language Models (LMs) often cannot be deployed because of their potential to harm users in hard-to-predict ways. Prior work identifies harmful behaviors before deployment by using human annotators to hand-write test cases.
Ethan Perez +8 more
semanticscholar +1 more source
A Survey of GPT-3 Family Large Language Models Including ChatGPT and GPT-4 [PDF]
Natural Language Processing Journal, 2023 Large language models (LLMs) are a special class of pretrained language models obtained by scaling model size, pretraining corpus and computation. LLMs, because of their large size and pretraining on large volumes of text data, exhibit special abilities ...
Katikapalli Subramanyam Kalyan
semanticscholar +1 more source
AudioLM: A Language Modeling Approach to Audio Generation [PDF]
IEEE/ACM Transactions on Audio Speech and Language Processing, 2022 We introduce AudioLM, a framework for high-quality audio generation with long-term consistency. AudioLM maps the input audio to a sequence of discrete tokens and casts audio generation as a language modeling task in this representation space. We show how
Zalán Borsos +10 more
semanticscholar +1 more source
ChatGPT for Language Teaching and Learning
RELC Journal : A Journal of Language Teaching and Research in Southeast Asia, 2023 In this technology review, we explore the affordances of the generative AI chatbot ChatGPT for language teaching and learning. In addition to this, we also present debates and drawbacks of ChatGPT.
Lucas Kohnke +2 more
semanticscholar +1 more source
SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing [PDF]
Conference on Empirical Methods in Natural Language Processing, 2018 This paper describes SentencePiece, a language-independent subword tokenizer and detokenizer designed for Neural-based text processing, including Neural Machine Translation. It provides open-source C++ and Python implementations for subword units.
Taku Kudo, John Richardson
semanticscholar +1 more source
BERTweet: A pre-trained language model for English Tweets [PDF]
Conference on Empirical Methods in Natural Language Processing, 2020 We present BERTweet, the first public large-scale pre-trained language model for English Tweets. Our BERTweet, having the same architecture as BERT-base (Devlin et al., 2019), is trained using the RoBERTa pre-training procedure (Liu et al., 2019 ...
Dat Quoc Nguyen, Thanh Vu, A. Nguyen
semanticscholar +1 more source
CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models [PDF]
Conference on Empirical Methods in Natural Language Processing, 2020 Pretrained language models, especially masked language models (MLMs) have seen success across many NLP tasks. However, there is ample evidence that they use the cultural biases that are undoubtedly present in the corpora they are trained on, implicitly ...
Nikita Nangia +3 more
semanticscholar +1 more source