Papers by Wanli Peng
Biology-Instructions: A Dataset and Benchmark for Multi-Omics Sequence Understanding Capability of Large Language Models (2025.findings-emnlp)
Copied to clipboard
Haonan He, Yuchen Ren, Yining Tang, Ziyang Xu, Junxian Li, Minghao Yang, Di Zhang, Yuan Dong, Tao Chen, Shufei Zhang, Yuqiang Li, Nanqing Dong, Wanli Ouyang, Dongzhan Zhou, Peng Ye
| Challenge: | Biology-Instructions is the first large-scale instruction-tuning dataset for multi-omics biological sequences. |
| Approach: | They propose a large-scale instruction-tuning dataset for multi-omics biological sequences . they propose 'chatMultiOmics' to overcome limitations of current LLMs on multi-ome tasks . |
| Outcome: | The proposed dataset bridges LLMs and complex biological sequence-related tasks while maintaining conversational fluency. |
Nature-Inspired Population-Based Evolution of Large Language Models (2026.acl-long)
Copied to clipboard
| Challenge: | a new framework for population-based evolution of large language models is emerging . a population-driven evolution of LLMs is a key component of evolution, authors say . |
| Approach: | They propose a framework that allows for population-based evolution of large language models . they start with a population of parent LLMs and allow this population to evolve . |
| Outcome: | The proposed framework outperforms existing methods on 12 datasets. |
ImF: Embedding an Implicit Fingerprint in Your Large Language Models (2026.acl-long)
Copied to clipboard
| Challenge: | Training and serving large language models (LLMs) is resource-intensive, making reliable intellectual property protection and black-box ownership verification increasingly important. |
| Approach: | They propose a method to inject a small set of secret query–response behaviors into model fingerprinting . they encode ownership information into a natural-looking target response and derive a semantically aligned query . |
| Outcome: | The proposed fingerprints improve stealthiness and remain verifiable under model updates and deployment-time prompt interventions. |
SLIP: Soft Label Mechanism and Key-Extraction-Guided CoT-based Defense Against Instruction Backdoor in APIs (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing black-box instruction backdoors can detect poisoned inputs, but fail to recover correct outputs once the backdoor is activated. |
| Approach: | They propose a soft label mechanism and key-extraction-guided CoT-based defense against instruction backdoors in APIs (SLIP) they propose KCOT-based model to extract task-relevant keywords and phrases rather than only considering the single trigger or overall text semantics. |
| Outcome: | The proposed model reduces the average attack success rate to 25.13% and improves clean accuracy to 87.15% and outperforms state-of-the-art black-box defenses. |
Kill two birds with one stone: generalized and robust AI-generated text detection via dynamic perturbations (2025.naacl-long)
Copied to clipboard
| Challenge: | Existing methods focus on model generalization or focus on robustness. |
| Approach: | They propose a model-based AIGT detection method that can be generalized and robust under two adversarial attacks. |
| Outcome: | The proposed method outperforms state-of-the-art methods for generalization and robustness under two text adversarial attacks. |
RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models (2024.findings-acl)
Copied to clipboard
Noah Wang, Z.y. Peng, Haoran Que, Jiaheng Liu, Wangchunshu Zhou, Yuhan Wu, Hongcheng Guo, Ruitong Gan, Zehao Ni, Jian Yang, Man Zhang, Zhaoxiang Zhang, Wanli Ouyang, Ke Xu, Wenhao Huang, Jie Fu, Junran Peng
| Challenge: | Large Language Models (LLMs) have paved the way for complex tasks such as role-playing. |
| Approach: | They propose a framework to benchmark, elicit, and enhance role-playing abilities in Large Language Models. |
| Outcome: | The proposed framework improves role-playing abilities with 168,093 samples. |
Inhibitory Attacks on Backdoor-based Fingerprinting for Large Language Models (2026.acl-long)
Copied to clipboard
| Challenge: | Backdoor-based LLM fingerprinting is a promising solution for intellectual property protection . however, the vulnerability of existing LLMs for the ensemble scenario is unexplored . |
| Approach: | They propose two new fingerprinting attack methods to assess the robustness of LLM fingerprinting by token filter attack and sentence verification attack. |
| Outcome: | The proposed methods inhibit the fingerprint response while maintaining ensemble performance. |
A Scalable Multi-LLM Collaboration System with Retrieval-based Selection and Exploration-Exploitation-Driven Enhancement (2026.acl-long)
Copied to clipboard
Shengji Tang, Jianjian Cao, Weihao Lin, Jiale Hong, Bo Zhang, Shuyue Hu, Lei Bai, Tao Chen, Wanli Ouyang, Peng Ye
| Challenge: | Existing multi-LLM collaboration systems often encounter scalability challenges when integrating new LLMs and tasks. |
| Approach: | They propose a Scalable Multi-LLM Collaboration System to coordinate multiple open-source LLMs. |
| Outcome: | The proposed system outperforms prevailing closed-source LLMs on eight mainstream benchmarks on multiple tasks. |