Papers by Tianyun Zhang
Efficient Transformer-based Large Scale Language Representations using Hardware-friendly Block Structured Pruning (2020.findings-emnlp)
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| Challenge: | Pretrained large-scale language models have been criticized for their limited weight storage and computational speed on hardware platforms. |
| Approach: | They propose an efficient transformer-based large-scale language representation using hardware-friendly block structure pruning. |
| Outcome: | The proposed model achieves 5.0x accuracy on GLUE benchmarks and 1.79x compression rate on DistilBERT. |
SOTOPIA-: Dynamic Strategy Injection Learning and Social Instruction Following Evaluation for Social Agents (2025.acl-long)
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| Challenge: | Existing studies on the social simulation of large language model intelligent agents have shown that even expert agents 1 perform significantly worse on challenging social tasks compared to expert agents. |
| Approach: | They propose a framework that dynamically injects a variety of social strategies into expert agents, thereby automating the construction of high-quality social dialogue training corpus. |
| Outcome: | The proposed framework enables the integration of social strategies into language agents and improves their performance on social tasks. |
Beyond Surface Alignment: Rebuilding LLMs Safety Mechanism via Probabilistically Ablating Refusal Direction (2025.findings-emnlp)
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| Challenge: | Jailbreak attacks pose persistent threats to large language models . current safety alignment methods have insufficient safety alignment depth and unrobust internal defense mechanisms. |
| Approach: | a new safety alignment framework is developed to overcome jailbreak attacks . the framework forces the model to dynamically rebuild its refusal mechanisms from jailbreak states . |
| Outcome: | a new safety alignment framework reduces attack success rates by approximately 95% on four open-source LLM families and six representative attacks. |
AwarenessBench: Assessing Cognitive Capabilities of Language Models (2026.acl-long)
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Xiaojian Li, Rongwu Xu, Tianyun Zhang, Yue Wang, Shuo Chen, Qiner Lyu, Briana Zhang, Peiran Yang, Kyle Xue Chen, Haoyuan Shi, Yu Wang, Wei Xu
| Challenge: | Language models exhibit increasingly consciousness-like behaviors, requiring a baseline to evaluate their cognitive abilities. |
| Approach: | They propose a benchmark to assess the cognitive abilities of language models (LMs) they compare 18 state-of-the-art LMs to human models in metacognition, self-awareness, social awareness and situational awareness . |
| Outcome: | Evaluating 18 state-of-the-art LMs, they find they consistently surpass baselines . but most models fall short in metacognition and self-awareness, the study finds . |