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Ting-Yun Chang
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2020 – today
- 2024
- [c11]Ting-Yun Chang, Jesse Thomason, Robin Jia:
When Parts Are Greater Than Sums: Individual LLM Components Can Outperform Full Models. EMNLP 2024: 10280-10299 - [c10]Ting-Yun Chang, Jesse Thomason, Robin Jia:
Do Localization Methods Actually Localize Memorized Data in LLMs? A Tale of Two Benchmarks. NAACL-HLT 2024: 3190-3211 - [i9]Ting-Yun Chang, Jesse Thomason, Robin Jia:
When Parts are Greater Than Sums: Individual LLM Components Can Outperform Full Models. CoRR abs/2406.13131 (2024) - 2023
- [c9]Ting-Yun Chang, Robin Jia
:
Data Curation Alone Can Stabilize In-context Learning. ACL (1) 2023: 8123-8144 - [i8]Ting-Yun Chang, Jesse Thomason, Robin Jia:
Do Localization Methods Actually Localize Memorized Data in LLMs? CoRR abs/2311.09060 (2023) - 2022
- [c8]Tejas Srinivasan, Ting-Yun Chang, Leticia Leonor Pinto Alva, Georgios Chochlakis, Mohammad Rostami, Jesse Thomason:
CLiMB: A Continual Learning Benchmark for Vision-and-Language Tasks. NeurIPS 2022 - [i7]Tejas Srinivasan, Ting-Yun Chang, Leticia Leonor Pinto Alva, Georgios Chochlakis, Mohammad Rostami, Jesse Thomason:
CLiMB: A Continual Learning Benchmark for Vision-and-Language Tasks. CoRR abs/2206.09059 (2022) - [i6]Ting-Yun Chang, Robin Jia:
Careful Data Curation Stabilizes In-context Learning. CoRR abs/2212.10378 (2022) - 2021
- [c7]Ting-Yun Chang, Chi-Jen Lu:
Rethinking Why Intermediate-Task Fine-Tuning Works. EMNLP (Findings) 2021: 706-713 - [c6]Ting-Yun Chang, Yang Liu, Karthik Gopalakrishnan, Behnam Hedayatnia, Pei Zhou, Dilek Hakkani-Tür:
Go Beyond Plain Fine-Tuning: Improving Pretrained Models for Social Commonsense. SLT 2021: 1028-1035 - [i5]Ting-Yun Chang, Yang Liu, Karthik Gopalakrishnan, Behnam Hedayatnia, Pei Zhou, Dilek Hakkani-Tür:
Incorporating Commonsense Knowledge Graph in Pretrained Models for Social Commonsense Tasks. CoRR abs/2105.05457 (2021) - [i4]Ting-Yun Chang, Yang Liu, Karthik Gopalakrishnan, Behnam Hedayatnia, Pei Zhou, Dilek Hakkani-Tür:
Go Beyond Plain Fine-tuning: Improving Pretrained Models for Social Commonsense. CoRR abs/2105.05913 (2021) - [i3]Ting-Yun Chang, Chi-Jen Lu:
Rethinking Why Intermediate-Task Fine-Tuning Works. CoRR abs/2108.11696 (2021) - 2020
- [c5]Ting-Yun Chang
TinyGAN: Distilling BigGAN for Conditional Image Generation. ACCV (4) 2020: 509-525 - [i2]Ting-Yun Chang, Chi-Jen Lu:
TinyGAN: Distilling BigGAN for Conditional Image Generation. CoRR abs/2009.13829 (2020)
2010 – 2019
- 2019
- [c4]Shang-Chi Tsai, Ting-Yun Chang, Yun-Nung Chen:
Leveraging Hierarchical Category Knowledge for Data-Imbalanced Multi-Label Diagnostic Text Understanding. LOUHI@EMNLP 2019: 39-43 - [c3]Ting-Yun Chang, Yun-Nung Chen:
What Does This Word Mean? Explaining Contextualized Embeddings with Natural Language Definition. EMNLP/IJCNLP (1) 2019: 6063-6069 - 2018
- [i1]Ting-Yun Chang, Ta-Chung Chi, Shang-Chi Tsai, Yun-Nung Chen:
xSense: Learning Sense-Separated Sparse Representations and Textual Definitions for Explainable Word Sense Networks. CoRR abs/1809.03348 (2018) - 2016
- [c2]Chao-Chun Liang, Shih-Hong Tsai, Ting-Yun Chang, Yi-Chung Lin, Keh-Yih Su:
A Meaning-based English Math Word Problem Solver with Understanding, Reasoning and Explanation. COLING (Demos) 2016: 151-155 - 2014
- [c1]Ting-Yun Chang, Cheng-Ta Yang:
Median thinking style predicts the individual differences in processing capacity. CogSci 2014
Coauthor Index
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