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Le modèle Transformer: un « couteau suisse » pour le traitement automatique des langues
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In: Techniques de l'Ingenieur ; https://hal.archives-ouvertes.fr/hal-03619077 ; Techniques de l'Ingenieur, Techniques de l'ingénieur, 2022, ⟨10.51257/a-v1-in195⟩ ; https://www.techniques-ingenieur.fr/base-documentaire/innovation-th10/innovations-en-electronique-et-tic-42257210/transformer-des-reseaux-de-neurones-pour-le-traitement-automatique-des-langues-in195/ (2022)
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Structured, flexible, and robust: comparing linguistic plans and explanations generated by humans and large language models ...
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Abstract:
How much can be learned about the structure of thinking from the statistics of language alone? Large language models -- neural models trained on next-word prediction tasks over large corpuses of text -- have made striking advances in modeling the statistical distribution of language. Sufficiently large corpuses contain language in which humans describe their beliefs and intentions, their goals and plans, and their stories about occurrences in real and imaginary worlds. Richly structured cognitive processes underlie this language that we produce; however, can such structure be captured when modeling distributional co-occurance of words alone? Is language modeling alone sufficiently flexible, accurate, and robust enough to generate language for novel, out-of-distribution queries, or are model-based approaches needed? In this study, we compare human and large-language-model performance on two domains which draw on structured, model-based thinking: 1) goal-based planning, and 2) explanation generation for causal ...
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Keyword:
Artificial Intelligence and Robotics; Computer Sciences; GPT-3; Language Models; Natural Language Processing; Physical Sciences and Mathematics; Planning
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URL: https://dx.doi.org/10.17605/osf.io/cy72b https://osf.io/cy72b/
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Easy-to-use combination of POS and BERT model for domain-specific and misspelled terms
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In: NL4IA Workshop Proceedings ; https://hal.archives-ouvertes.fr/hal-03474696 ; NL4IA Workshop Proceedings, Nov 2021, Milan, Italy (2021)
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Globalizing BERT-based Transformer Architectures for Long DocumentSummarization
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In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume ; 16th Conference of the European Chapter of the Association for Computational Linguistics ; https://hal.univ-grenoble-alpes.fr/hal-03367913 ; 16th Conference of the European Chapter of the Association for Computational Linguistics, Association for Computational Linguistics, Apr 2021, Online, France (2021)
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Navigating the Kaleidoscope of COVID-19 Misinformation Using Deep Learning ...
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HittER: Hierarchical Transformers for Knowledge Graph Embeddings ...
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HETFORMER: Heterogeneous Transformer with Sparse Attention for Long-Text Extractive Summarization ...
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Not All Negatives are Equal: Label-Aware Contrastive Loss for Fine-grained Text Classification ...
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Unsupervised Multi-View Post-OCR Error Correction With Language Models ...
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AttentionRank: Unsupervised Keyphrase Extraction using Self and Cross Attentions ...
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Automatic Fact-Checking with Document-level Annotations using BERT and Multiple Instance Learning ...
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Towards the Early Detection of Child Predators in Chat Rooms: A BERT-based Approach ...
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Semantic Categorization of Social Knowledge for Commonsense Question Answering ...
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Pre-train or Annotate? Domain Adaptation with a Constrained Budget ...
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Stepmothers are mean and academics are pretentious: What do pretrained language models learn about you? ...
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CLIFF: Contrastive Learning for Improving Faithfulness and Factuality in Abstractive Summarization ...
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Automatic Text Evaluation through the Lens of Wasserstein Barycenters ...
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