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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…
"I'm fully who I am": Towards Centering Transgender and Non-Binary Voices to Measure Biases in Open Language Generation
Anaelia Ovalle, Palash Goyal, Jwala Dhamala +5
Transgender and non-binary (TGNB) individuals disproportionately experience discrimination and exclusion from daily life. Given the recent popularity and adoption of language gener…
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…
ChatGPT for Us: Preserving Data Privacy in ChatGPT via Dialogue Text Ambiguation to Expand Mental Health Care Delivery
Anaelia Ovalle, Mehrab Beikzadeh, Parshan Teimouri +2
Large language models have been useful in expanding mental health care delivery. ChatGPT, in particular, has gained popularity for its ability to generate human-like dialogue. Howe…