Papers by Siyang Deng
Task-Adaptive Tokenization: Enhancing Long-Form Text Generation Efficacy in Mental Health and Beyond (2023.emnlp-main)
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| Challenge: | Existing methods to generate text in mental health are limiting, but they are effective for many tasks. |
| Approach: | They propose a task-adaptive tokenizer that allows for the integration of task-specific tokens into the pre-trained model's tokenization step. |
| Outcome: | The proposed tokenization approach improves generation performance on psychological question-answering tasks in Chinese and English while using 60% fewer tokens. |
Distance between Relevant Information Pieces Causes Bias in Long-Context LLMs (2025.findings-acl)
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Runchu Tian, Yanghao Li, Yuepeng Fu, Siyang Deng, Qinyu Luo, Cheng Qian, Shuo Wang, Xin Cong, Zhong Zhang, Yesai Wu, Yankai Lin, Huadong Wang, Xiaojiang Liu
| Challenge: | Positional biases in large language models hinder their ability to process long inputs. |
| Approach: | They propose a benchmark to assess positional bias in large language models involving multiple pieces of relevant information. |
| Outcome: | The proposed benchmark assesses the performance of long-context language models by examining their models with different input lengths and tasks. |
Has It All Been Solved? Open NLP Research Questions Not Solved by Large Language Models (2024.lrec-main)
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Oana Ignat, Zhijing Jin, Artem Abzaliev, Laura Biester, Santiago Castro, Naihao Deng, Xinyi Gao, Aylin Ece Gunal, Jacky He, Ashkan Kazemi, Muhammad Khalifa, Namho Koh, Andrew Lee, Siyang Liu, Do June Min, Shinka Mori, Joan C. Nwatu, Veronica Perez-Rosas, Siqi Shen, Zekun Wang, Winston Wu, Rada Mihalcea
| Challenge: | Recent advances in large language models have led to misleading public discourse that “it’s all been solved.” |
| Approach: | They identify 14 research areas encompassing 45 research directions that require new research and are not directly solvable by LLMs. |
| Outcome: | The research areas identified are 45 research directions that require new research and are not directly solvable by LLMs. |
You Are What You Annotate: Towards Better Models through Annotator Representations (2023.findings-emnlp)
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| Challenge: | Annotator disagreement is ubiquitous in natural language processing tasks. |
| Approach: | They propose to model annotators' idiosyncrasies and account for their idioms by creating representations for each annotator and their annotations. |
| Outcome: | The proposed model improves on an existing dataset with eight annotators with inherent disagreements while increasing model size by 1%. |