Papers by Zhe Su
Is this the real life? Is this just fantasy? The Misleading Success of Simulating Social Interactions With LLMs (2024.emnlp-main)
Copied to clipboard
| Challenge: | Recent advances in large language models have enabled richer social simulations . however, the role of information asymmetry in these simulations has been overlooked . |
| Approach: | They develop an evaluation framework to simulate social interactions with LLMs in different settings. |
| Outcome: | The proposed framework performs better in unrealistic, omniscient simulation settings but struggles in those with information asymmetry. |
LenAtten: An Effective Length Controlling Unit For Text Summarization (2021.findings-acl)
Copied to clipboard
| Challenge: | Fixed length summarization (FLS) requires generating summaries with a preset number of characters or words. |
| Approach: | They propose a length control unit called LenAtten to break this trade-off by generating a short and coherent summary with the target length. |
| Outcome: | The proposed model improves controllability and ROGUE scores and generalizes well. |
PlotGen-Bench: Evaluating VLMs on Generating Visualization Code from Diverse Plots across Multiple Libraries (2026.findings-acl)
Copied to clipboard
| Challenge: | PlotGen-Bench evaluates vision-language models' ability to generate executable visualization code from plots under realistic and complex visualization requirements. |
| Approach: | They propose a benchmark to evaluate plot-to-code generation in vision-language models . they use Matplot, Matplos, Mat3D, Mat4D, and Mat4E to evaluate their performance . |
| Outcome: | The proposed benchmark covers 9 major categories, 30 subcategories, and 3 core tasks . it covers 2D, 3D and animated plots across 5 widely used visualization libraries. |
Glyph: Scaling Context Windows via Visual-Text Compression (2026.acl-long)
Copied to clipboard
Jiale Cheng, Yusen Liu, Xinyu Zhang, Yulin Fei, Wenyi Hong, Ruiliang Lyu, Weihan Wang, Zhe Su, Xiaotao Gu, Xiao Liu, Yushi Bai, Jie Tang, Hongning Wang, Minlie Huang
| Challenge: | Large language models (LLMs) traditionally represent text as sequences of discrete tokens . a long-context scaling problem requires processing more tokens more efficiently . |
| Approach: | They propose a framework that renders long texts into compact visual pages and processes them with a vision-language model. |
| Outcome: | The proposed framework renders long texts into compact visual pages and processes them with a vision-language model. |
CuMA: Aligning LLMs with Sparse Cultural Values via Demographic-Aware Mixture of Adapters (2026.acl-long)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have a global audience, so alignment must extend to cultural resonance. |
| Approach: | They propose a framework that frames alignment as a conditional capacity separation problem. |
| Outcome: | The proposed framework outperforms both dense baselines and semantic-only MoEs on three large language models. |
AI-LieDar : Examine the Trade-off Between Utility and Truthfulness in LLM Agents (2025.naacl-long)
Copied to clipboard
| Challenge: | LieDar is a framework to study how LLM-based agents navigate these scenarios in a multi-turn interactive setting. |
| Approach: | They propose a framework to study how LLM-based agents navigate these scenarios in an interactive multi-turn setting. |
| Outcome: | The proposed framework shows that all models are truthful less than 50% of the time, although truthfulness and goal achievement rates vary across models. |
Uncovering and Categorizing Social Biases in Text-to-SQL (2023.acl-long)
Copied to clipboard
| Challenge: | Existing Text-to-SQL models are trained on clean, neutral datasets, such as Spider and WikiSQl, but these models contain social bias at different rates. |
| Approach: | They propose to use data to map natural language utterances to SQL queries. |
| Outcome: | The proposed model can contain social bias at different rates in the downstream Text-to-SQL task. |