Portfolio item number 1
Short description of portfolio item number 1
Posts by Collection
portfolio
Portfolio item number 2
Short description of portfolio item number 2
projects
A Unified Evaluation of Textual Backdoor Learning: Frameworks and Benchmarks
Published:
NeurIPS 2022 Datasets & Benchmarks. The pipe of Openbackdoor toolkit: 
publications
A Unified Evaluation of Textual Backdoor Learning: Frameworks and Benchmarks
Published in NeurIPS Datasets & Benchmarks, 2022
The paper develop an open-source toolkit OpenBackdoor to foster the implementations and evaluations of textual backdoor learning, and propose a simple yet strong clustering-based defense baseline
Recommended citation: Cui G, Yuan L, He B, et al. A unified evaluation of textual backdoor learning: Frameworks and benchmarks[J]. Advances in Neural Information Processing Systems, 2022, 35: 5009-5023. https://arxiv.org/abs/2206.08514
Beat LLMs at Their Own Game: Zero-Shot LLM-Generated Text Detection via Querying ChatGPT
Published in EMNLP Main, 2023
The paper design a zero-shot black-box method for detecting LLM-generated texts. Compared with other detection methods, our method has better generalization ability and is more stable across various datasets.
Recommended citation: Biru Zhu, Lifan Yuan, Ganqu Cui, Yangyi Chen, Chong Fu, Bingxiang He, Yangdong Deng, Zhiyuan Liu, Maosong Sun, and Ming Gu. 2023. Beat LLMs at Their Own Game: Zero-Shot LLM-Generated Text Detection via Querying ChatGPT. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 7470–7483, Singapore. Association for Computational Linguistics. https://aclanthology.org/2023.emnlp-main.463/
ULTRAFEEDBACK: Boosting Language Models with Scaled AI Feedback
Published in ICML Poster, 2024
We finally present UltraFeedback, a large-scale, high-quality, and diversified AI feedback dataset, which contains over 1 million GPT-4 feedback for 250k user-assistant conversations from various aspects. Built upon UltraFeedback, we align a LLaMA-based model by best-of-n sampling and reinforcement learning, demonstrating its exceptional performance on chat benchmarks.
Recommended citation: Ultrafeedback: Boosting language models with high-quality feedback G Cui, L Yuan, N Ding, G Yao, B He, W Zhu, Y Ni, G Xie, Z Liu… - arXiv preprint arXiv:2310.01377, 2023 https://arxiv.org/abs/2310.01377
Tell Me More! Towards Implicit User Intention Understanding of Language Model Driven Agents
Published in ACL Main, 2024
We introduce Intention-in-Interaction (IN3), a novel benchmark designed to inspect users’ implicit intentions through explicit queries. Employing IN3, we empirically train Mistral-Interact, a powerful model that proactively assesses task vagueness, inquires user intentions, and refines them into actionable goals before starting downstream agent task execution.
Recommended citation: Tell Me More! Towards Implicit User Intention Understanding of Language Model Driven Agents C Qian, B He, Z Zhuang, J Deng, Y Qin, X Cong, Z Zhang, J Zhou, Y Lin, Z Liu, M Sun… - arXiv preprint arXiv:2402.09205, 2024 https://arxiv.org/abs/2402.09205
Zero-Shot Generalization during Instruction Tuning: Insights from Similarity and Granularity
Published in NeurIPS In Submission, 2024
We first demonstrate through multiple metrics that zero-shot generalization during instruction tuning happens very early. Next, we investigate the facilitation of zero-shot generalization from both data similarity and granularity perspectives, confirming that encountering highly similar and fine-grained training data earlier during instruction tuning, without the constraints of defined “tasks”, enables better generalization. Finally, we propose a more grounded training data arrangement method, Test-centric Multi-turn Arrangement, and show its effectiveness in promoting continual learning and further loss reduction.
Recommended citation: Zero-Shot Generalization during Instruction Tuning: Insights from Similarity and Granularity B He, N Ding, C Qian, J Deng, G Cui, L Yuan, H Gao, H Chen, Z Liu, M Sun… - arXiv preprint arXiv:2406.11721, 2024 https://arxiv.org/abs/2406.11721
talks
Talk 1 on Relevant Topic in Your Field
Published:
This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!
Conference Proceeding talk 3 on Relevant Topic in Your Field
Published:
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.