Papers by Tianlin Zhang
LLMs Assist NLP Researchers: Critique Paper (Meta-)Reviewing (2024.emnlp-main)
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Jiangshu Du, Yibo Wang, Wenting Zhao, Zhongfen Deng, Shuaiqi Liu, Renze Lou, Henry Zou, Pranav Narayanan Venkit, Nan Zhang, Mukund Srinath, Haoran Zhang, Vipul Gupta, Yinghui Li, Tao Li, Fei Wang, Qin Liu, Tianlin Liu, Pengzhi Gao, Congying Xia, Chen Xing, Cheng Jiayang, Zhaowei Wang, Ying Su, Raj Shah, Ruohao Guo, Jing Gu, Haoran Li, Kangda Wei, Zihao Wang, Lu Cheng, Surangika Ranathunga, Meng Fang, Jie Fu, Fei Liu, Ruihong Huang, Eduardo Blanco, Yixin Cao, Rui Zhang, Philip Yu, Wenpeng Yin
| Challenge: | a comparative analysis of paper (meta-)reviews by large language models (LLMs) aims to identify and distinguish LLMs from human activities . |
| Approach: | They present a comparative analysis to identify and distinguish LLM activities from human activities. |
| Outcome: | The proposed analysis aims to improve recognition of instances when someone implicitly uses LLMs for reviewing activities. |
OptiCo: Adaptive Distributed Training Optimization via Collaborative Agent Reasoning (2026.acl-long)
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| Challenge: | Existing distributed training frameworks are plagued by over-reliance on prior profiling and poor generalization across models/hardware. |
| Approach: | They propose a model-driven multi-agent framework that leverages Large Language Models to enable automatic and explainable distributed training strategy configuration. |
| Outcome: | The proposed framework outperforms expert-designed training strategies within 20 iterations. |
MentalBERT: Publicly Available Pretrained Language Models for Mental Healthcare (2022.lrec-1)
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| Challenge: | Existing pretrained language models for mental health detection are inadequate . one in four people worldwide suffers from mental disorders . |
| Approach: | They train and release two pretrained masked language models to benefit machine learning for mental healthcare research . they demonstrate that language representations pretrained in the target domain improve the performance of mental health detection tasks. |
| Outcome: | The proposed models improve mental health detection tasks on several benchmarks and are available for free. |
Uncovering Strategic Egoism Behaviors in Large Language Models (2026.findings-acl)
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| Challenge: | Extensive experiments on 9 proprietary LLMs reveal that SE behaviors are widespread . study identifies egoistic decision-making as a risk for large language models . |
| Approach: | They propose a benchmark to measure egoistic behavior in large language models . they propose toxicity, jailbreak vulnerability and a lightweight mitigation that reinforces situational constraints . |
| Outcome: | The proposed model has a 67.96% occurrence rate and frequently manifests as manipulative coercion. |
Towards Interpretable Mental Health Analysis with Large Language Models (2023.emnlp-main)
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| Challenge: | Existing studies on large language models lack adequate evaluations and prompting strategies for explainability. |
| Approach: | They evaluate the mental health analysis and emotional reasoning ability of large language models (LLMs) using 11 datasets across 5 tasks. |
| Outcome: | The proposed model shows strong in-context learning ability but still has a significant gap with advanced task-specific methods. |