no code implementations • 30 May 2024 • Yihe Deng, Pan Lu, Fan Yin, Ziniu Hu, Sheng Shen, James Zou, Kai-Wei Chang, Wei Wang
To further self-improve reasoning on the extracted visual information, we let the model reuse a small portion of existing instruction-tuning data and append its self-generated image descriptions to the prompts.
no code implementations • 28 Feb 2024 • Fan Yin, Jayanth Srinivasa, Kai-Wei Chang
We study how to characterize and predict the truthfulness of texts generated from large language models (LLMs), which serves as a crucial step in building trust between humans and LLMs.
1 code implementation • 17 Feb 2024 • Tianyi Yan, Fei Wang, James Y. Huang, Wenxuan Zhou, Fan Yin, Aram Galstyan, Wenpeng Yin, Muhao Chen
Instruction tuning has been used as a promising approach to improve the performance of large language models (LLMs) on unseen tasks.
1 code implementation • 31 Jan 2024 • Chujie Zheng, Fan Yin, Hao Zhou, Fandong Meng, Jie zhou, Kai-Wei Chang, Minlie Huang, Nanyun Peng
In this work, we investigate how LLMs' behavior (i. e., complying with or refusing user queries) is affected by safety prompts from the perspective of model representation.
1 code implementation • 1 Nov 2023 • Po-Nien Kung, Fan Yin, Di wu, Kai-Wei Chang, Nanyun Peng
Instruction tuning (IT) achieves impressive zero-shot generalization results by training large language models (LLMs) on a massive amount of diverse tasks with instructions.
1 code implementation • 1 Jun 2023 • Fan Yin, Jesse Vig, Philippe Laban, Shafiq Joty, Caiming Xiong, Chien-Sheng Jason Wu
Large language models (LLMs) have shown impressive performance in following natural language instructions to solve unseen tasks.
1 code implementation • 31 May 2023 • Chenghao Yang, Fan Yin, He He, Kai-Wei Chang, Xiaofei Ma, Bing Xiang
In practice, Shapley Values are often estimated with a small number of stochastic model evaluations.
2 code implementations • 31 May 2023 • Zhouxing Shi, Yihan Wang, Fan Yin, Xiangning Chen, Kai-Wei Chang, Cho-Jui Hsieh
The prevalence and strong capability of large language models (LLMs) present significant safety and ethical risks if exploited by malicious users.
1 code implementation • 23 May 2023 • Da Yin, Xiao Liu, Fan Yin, Ming Zhong, Hritik Bansal, Jiawei Han, Kai-Wei Chang
Instruction tuning has emerged to enhance the capabilities of large language models (LLMs) to comprehend instructions and generate appropriate responses.
1 code implementation • ICCV 2023 • Hritik Bansal, Nishad Singhi, Yu Yang, Fan Yin, Aditya Grover, Kai-Wei Chang
Multimodal contrastive pretraining has been used to train multimodal representation models, such as CLIP, on large amounts of paired image-text data.
1 code implementation • 22 Oct 2022 • Fan Yin, Yao Li, Cho-Jui Hsieh, Kai-Wei Chang
Finally, our analysis shows that the two types of uncertainty provided by \textbf{ADDMU} can be leveraged to characterize adversarial examples and identify the ones that contribute most to model's robustness in adversarial training.
1 code implementation • ACL 2022 • Fan Yin, Zhouxing Shi, Cho-Jui Hsieh, Kai-Wei Chang
We propose two new criteria, sensitivity and stability, that provide complementary notions of faithfulness to the existed removal-based criteria.
1 code implementation • ACL 2020 • Fan Yin, Quanyu Long, Tao Meng, Kai-Wei Chang
We conduct a thorough study to diagnose the behaviors of pre-trained language encoders (ELMo, BERT, and RoBERTa) when confronted with natural grammatical errors.
1 code implementation • ACL 2019 • Xiaoya Li, Fan Yin, Zijun Sun, Xiayu Li, Arianna Yuan, Duo Chai, Mingxin Zhou, Jiwei Li
In this paper, we propose a new paradigm for the task of entity-relation extraction.
Ranked #1 on Relation Extraction on ACE 2005 (Sentence Encoder metric)
2 code implementations • NeurIPS 2019 • Yuxian Meng, Wei Wu, Fei Wang, Xiaoya Li, Ping Nie, Fan Yin, Muyu Li, Qinghong Han, Xiaofei Sun, Jiwei Li
However, due to the lack of rich pictographic evidence in glyphs and the weak generalization ability of standard computer vision models on character data, an effective way to utilize the glyph information remains to be found.
Ranked #1 on Chinese Sentence Pair Classification on LCQMC
Chinese Dependency Parsing Chinese Named Entity Recognition +21