Papers by David Gu
Text Compression for Efficient Language Generation (2025.naacl-srw)
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| Challenge: | Existing models rely on sub-word tokens for text generation, but there is no evidence for a more efficient way to generate text. |
| Approach: | They propose a hierarchical transformer language model capable of text generation by compressing text into sentence embeddings and employing a sentence attention mechanism. |
| Outcome: | The proposed model achieves an up to an order of magnitude improvement in FLOPs efficiency and a threefold increase in runtime speed compared to equally-sized models in the low-size regime. |
Scaling Collaborative Effort with Agents (2026.findings-acl)
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Shannon Zejiang Shen, Valerie Chen, Ken Gu, Alexis Ross, Zixian Ma, Jillian Ross, Alex Gu, Chenglei Si, Wayne Chi, Andi Peng, Jocelyn J Shen, Ameet Talwalkar, Tongshuang Wu, David Sontag
| Challenge: | Current evaluations of agents focus on producing high-quality, final outputs in one shot, failing to account for the inherently iterative nature of many real-world problems. |
| Approach: | They propose a framework that captures how an agent’s utility grows with increasing user involvement. |
| Outcome: | The proposed framework captures how an agent’s utility grows with increasing user involvement, revealing a missing ingredient in agent design: the ability to sustain engagement and scaffold user understanding. |