| Challenge: | We create a set of nonce words and prompt GPT-3 to generate their dictionary definitions. |
| Approach: | They create a set of nonce words and prompt GPT-3 to generate their dictionary definitions. |
| Outcome: | The proposed model can process new words and make them 'neologisms' . it can also adapt to and extend a changing vocabulary, the authors found . |
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| Challenge: | Existing approaches to detect whether natural language sequences are metaphoric or literal focus on detecting the transfer of knowledge structures to pre-trained language models. |
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GPT-too: A Language-Model-First Approach for AMR-to-Text Generation (2020.acl-main)
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Manuel Mager, Ramón Fernandez Astudillo, Tahira Naseem, Md Arafat Sultan, Young-Suk Lee, Radu Florian, Salim Roukos
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How Much Do Language Models Copy From Their Training Data? Evaluating Linguistic Novelty in Text Generation Using RAVEN (2023.tacl-1)
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| Challenge: | Current language models generate high-quality text, but are they copying it or have they learned generalizable linguistic abstractions? |
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Meaning Representations for Natural Languages: Design, Models and Applications (2024.lrec-tutorials)
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| Challenge: | a tutorial reviews the design of common meaning representations and SoTA models for predicting meaning representation. |
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Computational Etymology and Word Emergence (2020.lrec-1)
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| Challenge: | etymology is the study of words' origins. |
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| Challenge: | Language models (LMs) are statistical models trained to assign probability to human-generated text. |
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| Challenge: | Pretraining ever-larger language models on massive corpora requires enormous amounts of compute. |
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Paraphrase Types for Generation and Detection (2023.emnlp-main)
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| Challenge: | Current approaches to paraphrase generation and detection ignore the intricate linguistic properties of language. |
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Meaning Representations for Natural Languages: Design, Models and Applications (2022.emnlp-tutorials)
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| Challenge: | This tutorial reviews the design of common meaning representations and SoTA models for predicting meaning representation models. |
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Systematicity in GPT-3’s Interpretation of Novel English Noun Compounds (2022.findings-emnlp)
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| Challenge: | e.g., stew skillet, swamp squash) are not fully compositional, but highly predictable based on whether the modifier and head refer to artifacts or natural kinds. |
| Approach: | They propose to compare the interpretations of novel English noun compounds with the large language model GPT-3, which is governed by interpretive principles. |
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