Papers by Mei Tan
Tiny-Attention Adapter: Contexts Are More Important Than the Number of Parameters (2022.emnlp-main)
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| Challenge: | Adapter-tuning is a paradigm that transfers a pretrained language model to downstream tasks . Previously proposed adapters are all feed-forward neural networks . |
| Approach: | They propose to use tiny-attention attention with extremely small per-head dimensionality as adapters to modify hidden states at each position . they propose to average multiple attention heads' weights during deployment to reduce its inference computation cost. |
| Outcome: | The proposed adapter outperforms other adapter-tuning methods on the GLUE benchmark . it uses attention with extremely small per-head dimensionality to modify hidden states . |
IDEAlign: Comparing Ideas of Large Language Models to Domain Experts (2026.eacl-long)
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| Challenge: | Large language models are increasingly used to produce open-ended, interpretive annotations. |
| Approach: | They propose to use LLM annotations to evaluate content and assess expert similarity . they propose to benchmark different similarity methods against human ratings . |
| Outcome: | The proposed method performs best but falls short of expert alignment . it is useful as a triage filter rather than a substitute for human review. |
EduCoder: An Open-Source Annotation System for Education Transcript Data (2026.acl-demo)
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Saad Ashraf, Jim Malamut, Vishal Kumar, Guanzhong Pan, HyunJi Nam, Mei Tan, Lucía Langlois, Liliana Carolina Santos-Deonizio, Helen Spencer Higgins, Dorottya Demszky
| Challenge: | Existing annotation tools do not support team-based workflows or access to instructional context . |
| Approach: | They present an open-source web platform for annotating classroom conversation transcripts . they argue that existing annotation tools do not provide access to instructional context . |
| Outcome: | EduCoder is an open-source web platform designed for annotating classroom conversation transcripts. |
CaseSumm: A Large-Scale Dataset for Long-Context Summarization from U.S. Supreme Court Opinions (2025.findings-naacl)
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| Challenge: | CaseSumm is a dataset for long-context summarization in the legal domain . human groundtruth summaries are often not available for legal summarizing . |
| Approach: | They propose a dataset for long-context summarization that includes SCOTUS opinions and their official summaries. |
| Outcome: | The proposed dataset is the largest open legal case summarization dataset . it outperforms larger models on automatic metrics and human evaluation . |
Rejection-to-Acceptance Transition: Model Editing-Based Jailbreak Backdoor Injection Not Limited to Few Output Tokens (2026.findings-acl)
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Shiji Yang, Min Cai, Hao Xiong, Congyao Mei, Haodong Zou, Shicheng Tan, Jie Chen, Fulan Qian, Shu Zhao
| Challenge: | Existing methods for jailbreaking LLMs are implemented by binding backdoors to predefined phrases as first few output tokens, inducing the LLM’s next-token prediction to produce continuous responses. |
| Approach: | They propose a model editing-based jailbreak backdoor attack that hijacks LLM representations into a acceptance domain rather than binding to a few output tokens. |
| Outcome: | The proposed model editing method outperforms existing methods, showing stronger jailbreak capabilities across LLMs and datasets. |