Results 251 to 260 of about 1,400,670 (295)

ListT5: Listwise Reranking with Fusion-in-Decoder Improves Zero-shot Retrieval

Annual Meeting of the Association for Computational Linguistics
We propose ListT5, a novel reranking approach based on Fusion-in-Decoder (FiD) that handles multiple candidate passages at both train and inference time.
Soyoung Yoon, Eunbi Choi, Jiyeon Kim, Yireun Kim, Hyeongu Yun, Seung-won Hwang   +5 more
semanticscholar   +1 more source

Learning to Generate Instruction Tuning Datasets for Zero-Shot Task Adaptation

Annual Meeting of the Association for Computational Linguistics
We introduce Bonito, an open-source model for conditional task generation that converts unannotated text into task-specific training datasets for instruction tuning.
Nihal V. Nayak, Yiyang Nan, Avi Trost, Stephen H. Bach   +3 more
semanticscholar   +1 more source

MILL: Mutual Verification with Large Language Models for Zero-Shot Query Expansion

North American Chapter of the Association for Computational Linguistics, 2023
Query expansion, pivotal in search engines, enhances the representation of user information needs with additional terms. While existing methods expand queries using retrieved or generated contextual documents, each approach has notable limitations ...
Pengyue Jia, Yiding Liu, Xiangyu Zhao, Xiaopeng Li, Changying Hao, Shuaiqiang Wang, Dawei Yin   +6 more
semanticscholar   +1 more source

Revisiting Zero-Shot Abstractive Summarization in the Era of Large Language Models from the Perspective of Position Bias

North American Chapter of the Association for Computational Linguistics
We characterize and study zero-shot abstractive summarization in Large Language Models (LLMs) by measuring position bias, which we propose as a general formulation of the more restrictive lead bias phenomenon studied previously in the literature ...
Anshuman Chhabra, Hadi Askari, Prasant Mohapatra   +2 more
semanticscholar   +1 more source

Improving Event Definition Following For Zero-Shot Event Detection

Annual Meeting of the Association for Computational Linguistics
Existing approaches on zero-shot event detection usually train models on datasets annotated with known event types, and prompt them with unseen event definitions. These approaches yield sporadic successes, yet generally fall short of expectations.
Zefan Cai, Po-Nien Kung, Ashima Suvarna, Mingyu Derek Ma, Hritik Bansal, Baobao Chang, P. Brantingham, Wei Wang, Nanyun Peng   +8 more
semanticscholar   +1 more source

VisLingInstruct: Elevating Zero-Shot Learning in Multi-Modal Language Models with Autonomous Instruction Optimization

North American Chapter of the Association for Computational Linguistics
This paper presents VisLingInstruct, a novel approach to advancing Multi-Modal Language Models (MMLMs) in zero-shot learning. Current MMLMs show impressive zero-shot abilities in multi-modal tasks, but their performance depends heavily on the quality of ...
Dongsheng Zhu, Xunzhu Tang, Weidong Han, Jinghui Lu, Yukun Zhao, Guoliang Xing, Junfeng Wang, Dawei Yin   +7 more
semanticscholar   +1 more source

Analysing Zero-Shot Readability-Controlled Sentence Simplification

International Conference on Computational Linguistics
Readability-controlled text simplification (RCTS) rewrites texts to lower readability levels while preserving their meaning. RCTS models often depend on parallel corpora with readability annotations on both source and target sides.
Abdullah Barayan, José Camacho-Collados, Fernando Alva-Manchego   +2 more
semanticscholar   +1 more source

Autonomous Data Selection with Zero-shot Generative Classifiers for Mathematical Texts

Annual Meeting of the Association for Computational Linguistics
We present Autonomous Data Selection (AutoDS), a method that leverages base language models themselves as zero-shot"generative classifiers"to automatically curate high-quality mathematical texts.
Yifan Zhang, Yifan Zhang, Yifan Luo, Yang Yuan, A. C. Yao   +4 more
semanticscholar   +1 more source

Smaller Language Models are Better Zero-shot Machine-Generated Text Detectors

Conference of the European Chapter of the Association for Computational Linguistics
As large language models are becoming more embedded in different user-facing services, it is important to be able to distinguish between human-written and machine-generated text to verify the authenticity of news articles, product reviews, etc.
Niloofar Mireshghallah, Justus Mattern, Sicun Gao, Reza Shokri, Taylor Berg-Kirkpatrick   +4 more
semanticscholar   +1 more source

Tree-of-Counterfactual Prompting for Zero-Shot Stance Detection

Annual Meeting of the Association for Computational Linguistics
Stance detection enables the inference of atti-001 tudes from human communications. Automatic 002 stance identification was mostly cast as a classi-003 fication problem. However, stance decisions in-004 volve complex judgments, which can be nowa-005 days
Maxwell Weinzierl, S. Harabagiu
semanticscholar   +1 more source