Yuetai Li

dblp:352/0019 · DBLP profile ↗
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6ranked-venue papers
4as first author
6since 2021 · last 2026
—ORCID · conflict

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 3 · 2 first-author · 3 since 2021Computer networks · 2 · 2 first-author · 2 since 2021Systems, architecture and hardware · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
3 papers
Language models and text generation · 60% Trustworthy machine learning · 21% Multi-agent systems · 20%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
Distributed systems · 87% Performance modeling and evaluation · 13%
Network and information security
1 paper
Security and privacy of machine learning · 77% Systems and software security · 23%

Topics — the 8 heaviest of 13, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Natural language and speech › Language models and text generation
large language model evaluation
1.012026
BadScientist: Can a Research Agent Write Convincing but Unsound Papers that Fool LLM Reviewers? · ACL (1) 2026
Distributed systems › fault tolerance
byzantine fault tolerance
1.012026
Distributed Consensus Network: A Modularized Communication Framework and Reliability Probabilistic Analysis · IEEE Trans. Netw. 2026
Distributed systems
consensus
1.012026
Distributed Consensus Network: A Modularized Communication Framework and Reliability Probabilistic Analysis · IEEE Trans. Netw. 2026
Machine learning › Trustworthy machine learning › robustness
backdoor defense
0.812024
CleanGen: Mitigating Backdoor Attacks for Generation Tasks in Large Language Models · EMNLP 2024
Natural language and speech › Language models and text generation › trustworthy language model
large language model security
0.812024
CleanGen: Mitigating Backdoor Attacks for Generation Tasks in Large Language Models · EMNLP 2024
Security and privacy of machine learning › adversarial attack
backdoor attack
0.812024
CleanGen: Mitigating Backdoor Attacks for Generation Tasks in Large Language Models · EMNLP 2024
Natural language and speech › Language models and text generation › chain-of-thought reasoning
long chain-of-thought reasoning
0.312026
Temporal Sampling for Forgotten Reasoning in LLMs · ACL (1) 2026
Machine learning › Trustworthy machine learning
robustness
0.312026
BadScientist: Can a Research Agent Write Convincing but Unsound Papers that Fool LLM Reviewers? · ACL (1) 2026

Methods — techniques the papers use, named apart from their topics

generation filtering · 1.5backdoor detection · 1.5temporal sampling · 1.0probabilistic analysis · 1.0modular communication framework · 1.0
YearPublicationVenuePosition
2026 BadScientist: Can a Research Agent Write Convincing but Unsound Papers that Fool LLM Reviewers?
abstract
Fengqing Jiang, Yichen Feng, Yuetai Li, Luyao Niu, Basel Alomair, Radha Poovendran. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.
Fengqing Jiang, Yichen Feng, Yuetai Li, Luyao Niu, Basel Alomair, Radha Poovendran
ACL (1)3
2026 Temporal Sampling for Forgotten Reasoning in LLMs
abstract
Yuetai Li, Zhangchen Xu, Fengqing Jiang, Bhaskar Ramasubramanian, Luyao Niu, Bill Yuchen Lin, Xiang Yue, Radha Poovendran. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.
Yuetai Li, Zhangchen Xu, Fengqing Jiang, Bhaskar Ramasubramanian, Luyao Niu, Bill Y. Lin, Xiang Yue, Radha Poovendran
ACL (1)1
2026 Distributed Consensus Network: A Modularized Communication Framework and Reliability Probabilistic Analysis
abstract
In this paper, we propose a modularized framework for communication processes applicable to crash and Byzantine fault-tolerant consensus protocols. We abstract basic communication components and show that the communication process of the classic consensus protocols such as RAFT, single-decree Paxos, PBFT, and Hotstuff, can be represented by the combination of communication components. Based on the proposed framework, we develop an approach to analyze the consensus reliability of different protocols, where link loss and node failure are measured as a probability. We propose two latency optimization methods and implement a RAFT system to verify our theoretical analysis and the effectiveness of the proposed latency optimization methods. We also discuss decreasing consensus failure rate by adjusting protocol designs. This paper provides theoretical guidance for the design of future consensus systems with a low consensus failure rate and latency under the possible communication loss.
Yuetai Li, Zhangchen Xu, Zihan Zhou 0019, Lei Zhang 0035, Jon Crowcroft
IEEE Trans. Netw.1
2024 CleanGen: Mitigating Backdoor Attacks for Generation Tasks in Large Language Models
abstract
Yuetai Li, Zhangchen Xu, Fengqing Jiang, Luyao Niu, Dinuka Sahabandu, Bhaskar Ramasubramanian, Radha Poovendran. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. 2024.
Yuetai Li, Zhangchen Xu, Fengqing Jiang, Luyao Niu, Dinuka Sahabandu, Bhaskar Ramasubramanian, Radha Poovendran
EMNLP1
2023 Exact Fault-Tolerant Consensus with Voting Validity
abstract
This paper investigates the multi-valued fault-tolerant distributed consensus problem that pursues exact output. To this end, the voting validity, which requires the consensus output of non-faulty nodes to be the exact plurality of the input of non-faulty nodes, is investigated. Considering a specific distribution of non-faulty votes, we first give the impossibility results and a tight lower bound of system tolerance achieving agreement, termination and voting validity. A practical consensus algorithm that satisfies voting validity in the Byzantine fault model is proposed subsequently. To ensure the exactness of outputs in any non-faulty vote distribution, we further propose safety-critical tolerance and a corresponding protocol that prioritizes voting validity over termination property. To refine the proposed protocols, we propose an incremental threshold algorithm that accelerates protocol operation speed. We also optimize consensus algorithms with the local broadcast model to enhance the protocol’s fault tolerance ability.
Zhangchen Xu, Yuetai Li, Chenglin Feng, Lei Zhang 0035
IPDPS2
2023 RAFT Consensus Reliability in Wireless Networks: Probabilistic Analysis
abstract
The centralized system becomes less efficient, secure, and resilient as the network size and heterogeneity increase due to its inherent single point of failure issues. Distributed consensus mechanisms characterized by decentralization, autonomy, parallelism, and fault-tolerance can meet the increasing demands of safety and security in critical interconnected systems. This article establishes a Node and Link probabilistic failure model in the presence of node and communication link failures for a representative crash fault-tolerant distributed consensus protocol: RAFT. The analytical results in terms of the probability density function and the mean value of consensus reliability are derived. Two important reliability performance indicators, Reliability Gain and Tolerance Gain are proposed to indicate the linear relationship between the consensus reliability and two basic parameters, i.e., the joint failure rate and the maximum number of tolerant faulty nodes, which provide the theoretical guidance for quickly deploying an RAFT system. The special case of a distributed consensus network with already a certain number of failures and its adverse impact are evaluated. The Markov probabilistic models, definitions of Reliability Gain and Tolerance Gain, and the analysis methods proposed in this article can be extended to other consensus mechanisms.
Yuetai Li, Yixuan Fan, Lei Zhang 0035, Jon Crowcroft
IEEE Internet Things J.1