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MedAgents: Large Language Models as Collaborators for Zero-shot Medical Reasoning
Annual Meeting of the Association for Computational Linguistics, 2023Large language models (LLMs), despite their remarkable progress across various general domains, encounter significant barriers in medicine and healthcare.
Xiangru Tang +6 more
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Beyond Yes and No: Improving Zero-Shot LLM Rankers via Scoring Fine-Grained Relevance Labels
North American Chapter of the Association for Computational Linguistics, 2023Zero-shot text rankers powered by recent LLMs achieve remarkable ranking performance by simply prompting. Existing prompts for pointwise LLM rankers mostly ask the model to choose from binary relevance labels like “Yes” and “No”.
Honglei Zhuang +6 more
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Conference of the European Chapter of the Association for Computational Linguistics, 2023
Current developments in large language models (LLMs) have enabled impressive zero-shot capabilities across various natural language tasks. An interesting application of these systems is in the automated assessment of natural language generation (NLG), a ...
Adian Liusie, Potsawee Manakul, M. Gales
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Current developments in large language models (LLMs) have enabled impressive zero-shot capabilities across various natural language tasks. An interesting application of these systems is in the automated assessment of natural language generation (NLG), a ...
Adian Liusie, Potsawee Manakul, M. Gales
semanticscholar +1 more source
LM-Infinite: Zero-Shot Extreme Length Generalization for Large Language Models
North American Chapter of the Association for Computational Linguistics, 2023Today’s large language models (LLMs) typically train on short text segments (e.g.,
Chi Han +6 more
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Self-Improving for Zero-Shot Named Entity Recognition with Large Language Models
North American Chapter of the Association for Computational Linguistics, 2023Exploring the application of powerful large language models (LLMs) on the named entity recognition (NER) task has drawn much attention recently. This work pushes the performance boundary of zero-shot NER with LLMs by proposing a training-free self ...
Tingyu Xie +4 more
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VoiceCraft: Zero-Shot Speech Editing and Text-to-Speech in the Wild
Annual Meeting of the Association for Computational LinguisticsWe introduce VoiceCraft, a token infilling neural codec language model, that achieves state-of-the-art performance on both speech editing and zero-shot text-to-speech (TTS) on audiobooks, internet videos, and podcasts.
Puyuan Peng +4 more
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LSTPrompt: Large Language Models as Zero-Shot Time Series Forecasters by Long-Short-Term Prompting
Annual Meeting of the Association for Computational LinguisticsTime-series forecasting (TSF) finds broad applications in real-world scenarios. Prompting off-the-shelf Large Language Models (LLMs) demonstrates strong zero-shot TSF capabilities while preserving computational efficiency.
Haoxin Liu +4 more
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Large Language Models as Zero-shot Dialogue State Tracker through Function Calling
Annual Meeting of the Association for Computational LinguisticsLarge language models (LLMs) are increasingly prevalent in conversational systems due to their advanced understanding and generative capabilities in general contexts.
Zekun Li +9 more
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Rethinking Task-Oriented Dialogue Systems: From Complex Modularity to Zero-Shot Autonomous Agent
Annual Meeting of the Association for Computational LinguisticsTask-oriented dialogue (TOD) systems are pre-001 dominantly designed to be composed of several 002 functional modules (e.g. dialogue state tracker, 003 dialogue policy, natural language generation) 004 whether they are pipeline or end-to-end archi-005 ...
Heng-Da Xu +4 more
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