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13 papers · 2 filters
Self-Augmentation Improves Zero-Shot Cross-Lingual Transfer
Fei Wang, Kuan-Hao Huang, Kai-Wei Chang +1
Zero-shot cross-lingual transfer is a central task in multilingual NLP, allowing models trained in languages with more sufficient training resources to generalize to other low-reso…
MetaVL: Transferring In-Context Learning Ability From Language Models to Vision-Language Models
Masoud Monajatipoor, Liunian Harold Li, Mozhdeh Rouhsedaghat +2
Large-scale language models have shown the ability to adapt to a new task via conditioning on a few demonstrations (i.e., in-context learning). However, in the vision-language doma…
Symbolic Chain-of-Thought Distillation: Small Models Can Also "Think" Step-by-Step
Liunian Harold Li, Jack Hessel, Youngjae Yu +3
Chain-of-thought prompting (e.g., "Let's think step-by-step") primes large language models to verbalize rationalization for their predictions. While chain-of-thought can lead to dr…
Efficient Shapley Values Estimation by Amortization for Text Classification
Chenghao Yang, Fan Yin, He He +3
Despite the popularity of Shapley Values in explaining neural text classification models, computing them is prohibitive for large pretrained models due to a large number of model e…
PIP: Parse-Instructed Prefix for Syntactically Controlled Paraphrase Generation
Yixin Wan, Kuan-Hao Huang, Kai-Wei Chang
Syntactically controlled paraphrase generation requires language models to generate paraphrases for sentences according to specific syntactic structures. Existing fine-tuning metho…
ParaAMR: A Large-Scale Syntactically Diverse Paraphrase Dataset by AMR Back-Translation
Kuan-Hao Huang, Varun Iyer, I-Hung Hsu +3
Paraphrase generation is a long-standing task in natural language processing (NLP). Supervised paraphrase generation models, which rely on human-annotated paraphrase pairs, are cos…