Zhi Rui Tam

Zhi Rui Tam

male / home sapiens

( ´ ω ` )

Publications

You can also find my articles on my Google Scholar profile.

Conference Papers

Let Me Speak Freely? A Study On The Impact Of Format Restrictions On Large Language Model Performance

Published in EMNLP Industry Track, 2024

Structured generation, the process of producing content in standardized formats like JSON and XML, is widely utilized in real-world applications to extract key output information from large language models (LLMs).

Recommended citation: Tam, Z.R., Wu, C.K., Tsai, Y.L., Lin, C.Y., Lee, H., & Chen, Y.N. (2024). "Let Me Speak Freely? A Study On The Impact Of Format Restrictions On Large Language Model Performance." EMNLP Industry Track, 1218-1236.
Download Paper

I Need Help! Evaluating LLM’s Ability to Ask for Users’ Support: A Case Study on Text-to-SQL Generation

Published in arXiv, 2024

This study explores the proactive ability of LLMs to seek user support. We propose metrics to evaluate the trade-off between performance improvements and user burden, and investigate whether LLMs can determine when to request help under varying information availability.

Recommended citation: Wu, C.K., Tam, Z.R., Wu, C.C., Lin, C.Y., Lee, H., & Chen, Y.N. (2024). "I Need Help! Evaluating LLM's Ability to Ask for Users' Support: A Case Study on Text-to-SQL Generation." arXiv preprint arXiv:2407.14767.
Download Paper

Personalized EDM Subject Generation via Co-factored User-Subject Embedding

Published in PAKDD, 2024

This paper introduces the Co-Factored User-Subject Embedding based Personalized EDM Subject Generation Framework (COUPES), a model for creating personalized Electronic Direct Mail (EDM) subjects.

Recommended citation: Chen, Y.H., Tam, Z.R., & Shuai, H.H. (2024). "Personalized EDM Subject Generation via Co-factored User-Subject Embedding." Pacific-Asia Conference on Knowledge Discovery and Data Mining, 55-67.
Download Paper

An improved traditional chinese evaluation suite for foundation model

Published in arXiv, 2024

We present TMMLU+, a new benchmark designed for Traditional Chinese language understanding. TMMLU+ is a multi-choice question-answering dataset with 66 subjects from elementary to professional level. It is six times larger and boasts a more balanced subject distribution than its predecessor, Taiwan Massive Multitask Language Understanding (TMMLU).

Recommended citation: Tam, Z.R., Pai, Y.T., Lee, Y.W., Chen, J.D., Chu, W.M., Cheng, S., & Shuai, H.H. (2024). "An improved traditional chinese evaluation suite for foundation model." arXiv preprint arXiv:2403.01858.
Download Paper

Openassistant conversations-democratizing large language model alignment

Published in NeurIPS, 2024

Aligning large language models (LLMs) with human preferences has proven to drastically improve usability and has driven rapid adoption as demonstrated by ChatGPT.

Recommended citation: Köpf, A., Kilcher, Y., von Rütte, D., Anagnostidis, S., Tam, Z.R., et al. (2024). "Openassistant conversations-democratizing large language model alignment." NeurIPS, 36.
Download Paper

Improving entity disambiguation using knowledge graph regularization

Published in PAKDD, 2022

Entity disambiguation plays the role on bridging between words of interest from an input text document and unique entities in a target Knowledge Base (KB).

Recommended citation: Tam, Z.R., Wu, Y.L., & Shuai, H.H. (2022). "Improving entity disambiguation using knowledge graph regularization." Pacific-Asia Conference on Knowledge Discovery and Data Mining, 341-353.
Download Paper

Gradient normalization for generative adversarial networks

Published in ICCV, 2021

In this paper, we propose a novel normalization method called gradient normalization (GN) to tackle the training instability of Generative Adversarial Networks (GANs) caused by the sharp gradient space.

Recommended citation: Wu, Y.L., Shuai, H.H., Tam, Z.R., & Chiu, H.Y. (2021). "Gradient normalization for generative adversarial networks." ICCV, 6373-6382.
Download Paper

Character-preserving coherent story visualization

Published in ECCV, 2020

Story visualization aims at generating a sequence of images to narrate each sentence in a multi-sentence story.

Recommended citation: Song, Y.Z., Tam, Z.R., Chen, H.J., Lu, H.H., & Shuai, H.H. (2020). "Character-preserving coherent story visualization." European Conference on Computer Vision, 18-33.
Download Paper