Transforming scholarly landscapes: The influence of large language models on academic fields beyond computer science. [PDF]
PLoS ONELarge Language Models (LLMs) have ushered in a transformative era in Natural Language Processing (NLP), reshaping research and extending NLP's influence to other fields of study.
Aniket Pramanick +3 more
doaj +2 more sources
"We must work tirelessly to promote …": Mediating interpersonal commitment to material processes in English translation of Chinese political discourse. [PDF]
PLoS ONEEvaluative items have been central in political translation studies. This paper extends previous research by focusing on "circumstances + material processes" expressions through data extracted from Chinese-English parallel corpus of Xi Jinping's ...
Jiachun Li, Shukun Chen, Yawen Zhang
doaj +2 more sources
Combining computational linguistics with sentence embedding to create a zero-shot NLIDB
ArrayAccessing relational databases using natural language is a challenging task, with existing methods often suffering from poor domain generalization and high computational costs. In this study, we propose a novel approach that eliminates the training phase
Yuriy Perezhohin +2 more
doaj +2 more sources
Promises and pitfalls of using LLMs to identify actor stances in political discourse. [PDF]
PLoS ONEEmpirical research in the social sciences is often interested in understanding actor stances; the positions that social actors take regarding normative statements in societal discourse.
Viviane Walker, Mario Angst
doaj +2 more sources
Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models [PDF]
Annual Meeting of the Association for Computational Linguistics, 2023 Large language models (LLMs) have recently been shown to deliver impressive performance in various NLP tasks. To tackle multi-step reasoning tasks, Few-shot chain-of-thought (CoT) prompting includes a few manually crafted step-by-step reasoning ...
Lei Wang +6 more
semanticscholar +1 more source
Better Zero-Shot Reasoning with Role-Play Prompting [PDF]
North American Chapter of the Association for Computational Linguistics, 2023 Modern large language models (LLMs) exhibit a remarkable capacity for role-playing, enabling them to embody not only human characters but also non-human entities.
Aobo Kong +6 more
semanticscholar +1 more source
Precise Zero-Shot Dense Retrieval without Relevance Labels [PDF]
Annual Meeting of the Association for Computational Linguistics, 2022 While dense retrieval has been shown to be effective and efficient across tasks and languages, it remains difficult to create effective fully zero-shot dense retrieval systems when no relevance labels are available.
Luyu Gao +3 more
semanticscholar +1 more source
Zero-shot Cross-Linguistic Learning of Event Semantics
Proceedings of the 15th International Conference on Natural Language Generation, 2022 Typologically diverse languages offer systems of lexical and grammatical aspect that allow speakers to focus on facets of event structure in ways that comport with the specific communicative setting and discourse constraints they face. In this paper, we look specifically at captions of images across Arabic, Chinese, Farsi, German, Russian, and Turkish ...
Alikhani, Malihe +8 more
openaire +4 more sources
Aligning Instruction Tasks Unlocks Large Language Models as Zero-Shot Relation Extractors [PDF]
Annual Meeting of the Association for Computational Linguistics, 2023 Recent work has shown that fine-tuning large language models (LLMs) on large-scale instruction-following datasets substantially improves their performance on a wide range of NLP tasks, especially in the zero-shot setting.
Kai Zhang +2 more
semanticscholar +1 more source
Gaussian Visual-Linguistic Embedding for Zero-Shot Recognition [PDF]
Proceedings of the 2016 Conference on Empirical Methods in Natural
Language Processing, 2016 An exciting outcome of research at the intersection of language and vision is that of zeroshot learning (ZSL). ZSL promises to scale visual recognition by borrowing distributed semantic models learned from linguistic corpora and turning them into visual recognition models.
Tanmoy Mukherjee, Timothy Hospedales
openaire +3 more sources