Lin Ai

Congratulations to this year's deserving winner, whose dedication has significantly shaped our field! Their service is an inspiration to us all. ✨ #ACL2025NLP #DistinguishedServiceAward #CompLing #Community

Productive week at #coling2025 in Abu Dhabi! 🐪🌴Presented 2 papers 📄: 1️⃣ NoVAScore: Evaluating Novelty – aclanthology.org/2025.coling-ma… 2️⃣ PropaInsight: Understanding Propaganda – aclanthology.org/2025.coling-ma… Grateful for my amazing collaborators!”

Everything you love about generative models — now powered by real physics! Announcing the Genesis project — after a 24-month large-scale research collaboration involving over 20 research labs — a generative physics engine able to generate 4D dynamical worlds powered by a physics simulation platform designed for general-purpose robotics and physical AI applications. Genesis's physics engine is developed in pure Python, while being 10-80x faster than existing GPU-accelerated stacks like Isaac Gym and MJX. It delivers a simulation speed ~430,000 faster than in real-time, and takes only 26 seconds to train a robotic locomotion policy transferrable to the real world on a single RTX4090 (see tutorial: genesis-world.readthedocs.io/en/latest/user…). The Genesis physics engine and simulation platform is fully open source at github.com/Genesis-Embodi…. We'll gradually roll out access to our generative framework in the near future. Genesis implements a unified simulation framework all from scratch, integrating a wide spectrum of state-of-the-art physics solvers, allowing simulation of the whole physical world in a virtual realm with the highest realism. We aim to build a universal data engine that leverages an upper-level generative framework to autonomously create physical worlds, together with various modes of data, including environments, camera motions, robotic task proposals, reward functions, robot policies, character motions, fully interactive 3D scenes, open-world articulated assets, and more, aiming towards fully automated data generation for robotics, physical AI and other applications. Open Source Code: github.com/Genesis-Embodi… Project webpage: genesis-embodied-ai.github.io Documentation: genesis-world.readthedocs.io 1/n

Here’s 2/2 of my #coling2025 papers: NoVAScore🌟 We introduce an automated metric to assess document novelty and salience. Huge thanks to my coauthor @SaraZiweiGong and the team for their amazing collaboration! Read here: arxiv.org/pdf/2409.09249

Excited to share 1/2 of my #coling2025 papers: PropaInsight: Toward Deeper Understanding of Propaganda! Huge thanks to my coauthors @JiatengLiu, @May_F1_ and team, and special thanks to @juliahberg, @hengjinlp, @preslav_nakov for their support! Read here: arxiv.org/pdf/2409.18997

Over an amazing week in Miami, I presented 3 papers at #EMNLP2024! 1️⃣ QASE: Engancing LLMs for MRC 2️⃣ ConvoSentinel: Defending Social Engineering in LLMs 3️⃣ OpenIE Survey: From Rule-Based to LLMs Loved reuniting with old friends & meeting brilliant researchers! 🚀 #NLP #LLM

🎉 Excited to share our EMNLP 2024 paper! We introduce a module that enhances generative models for extractive MRC with minimal computational cost, bridging the gap between generative and extractive models. 🚀 Check it out: arxiv.org/pdf/2404.17991 #EMNLP2024 #NLP #LLMs

🚨 Thrilled to announce our EMNLP 2024 paper on defending against LLM-driven social engineering attacks, co-authored with @TharinduKTS, @Amrita_Bh, and more! Check it out: arxiv.org/pdf/2406.12263 🚀 #EMNLP2024 #NLP

Excited to share our EMNLP 2024 survey on Open Information Extraction, co-authored with @SaraZiweiGong, @pailiu1998, and more! 🎉 Grateful for such an amazing team. Check it out: arxiv.org/pdf/2208.08690 🚀 #EMNLP2024 #NLP

🎉 Excited to share that I have three papers accepted at EMNLP 2024, covering: enhancing PLMs for reading comprehension, defending against LLM-driven cyber-attacks, and a survey on OpenIE advancements! 🚀 #EMNLP2024 #NLP #AI #LLMs #MachineLearning