Meta-tuning Language Models to Answer Prompts Better

By Ruiqi Zhong, Kristy Lee, Zheng Zhang, Daniel Klein. 2021

13 citations recorded by the source corpus.

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In Prompting & Reasoning

Citations in the corpus

Finetuned Language Models Are Zero-Shot Learners (2021, methodology): “Focusing on binary text classification, Zhong et al. (2021) finetune T5-770M on 43 tasks phrased as yes/no questions and study the zero-shot performance on unseen tasks.”
LFPT5: A Unified Framework for Lifelong Few-shot Language Learning Based on Prompt Tuning of T5 (2021, background): “…Tam et al., 2021) and continuous or soft prompts (Li & Liang, 2021; Liu et al., 2021c; Lester et al., 2021), ( ii ) applications of PL (Han et al., 2021; Ben-David et al., 2021; Ding et al., 2021), and analysis of prompt-based learning (Liu et al., 2021a; Le Scao & Rush, 2021; Zhong et al., 2021).”
Text Data Augmentation for Deep Learning (2021, unknown): “In correspondence with the lesson of alignment, Zhong et al. [65] tune language models to be better fitted to answer prompts.”
UnifiedQA-v2: Stronger Generalization via Broader Cross-Format Training (2022, background): “Followup works have also replicated similar successes on more recent datasets that did not exist at the time of UNIFIEDQA’s construction (Hendrycks et al., 2020; Dasigi et al., 2021; Bragg et al., 2021; Wu et al., 2021; Zhong et al., 2021b) – further strengthening its empirical strength.”
Text Generation with Efficient (Soft) Q-Learning (2021, background): “Prominent examples include learning to generate prompts to control the massive LMs [65, 54, 67], learning text generation from noisy or even negative data, learning to generate adversarial text attacks for robustness study [60, 1], and others, where people have to devise specialized algorithms due…”