Papers by Kaiyue Li
T2I-ReasonBench: Benchmarking Reasoning-Informed Text-to-Image Generation (2026.findings-acl)
Copied to clipboard
| Challenge: | Text-to-image (T2I) generative models have demonstrated exceptional capability in synthesizing high-quality images from textual prompts. |
| Approach: | They propose a benchmark to explore the knowledge-driven reasoning capabilities of T2I models. |
| Outcome: | The proposed benchmark examines the knowledge-driven reasoning capabilities of T2I models. |
On Transferability of Prompt Tuning for Natural Language Processing (2022.naacl-main)
Copied to clipboard
Yusheng Su, Xiaozhi Wang, Yujia Qin, Chi-Min Chan, Yankai Lin, Huadong Wang, Kaiyue Wen, Zhiyuan Liu, Peng Li, Juanzi Li, Lei Hou, Maosong Sun, Jie Zhou
| Challenge: | Pre-trained language models (PLMs) can achieve comparable performance to full-parameter fine-tuning by tuning a few soft prompts, but require much more training time than fine-timing. |
| Approach: | They empirically investigate the transferability of soft prompts across different downstream tasks and PLMs to determine what decides prompt transferability. |
| Outcome: | The proposed method can achieve comparable performance to full-parameter fine-tuning by tuning a few soft prompts, but requires much more training time than fine-timing. |
Finding Skill Neurons in Pre-trained Transformer-based Language Models (2022.emnlp-main)
Copied to clipboard
| Challenge: | Pre-trained language models have demonstrated superior performance on various natural language processing tasks. |
| Approach: | They find that after prompt tuning, some neurons encode task-specific skills . they also show that skill neurons are most likely generated in pre-training . |
| Outcome: | The neurons are highly predictive of task labels after prompt tuning for specific tasks. |
HintPilot: LLM-based Compiler Hint Synthesis for Code Optimization (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing methods to optimize source code rely on invasive transformations that can introduce semantic errors and miss fine-grained compiler-level optimization opportunities. |
| Approach: | They propose a method that bridges LLM-based reasoning with traditional compilers by synthesizing compiler hints. |
| Outcome: | HintPilot achieves 6.88x speedup over -Ofast while preserving program correctness. |