Event Details

Speaker: Da Yin, UCLA

Conference Room Location: ISI-MDR CR#689

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https://usc.zoom.us/j/98977559622?pwd=IK59VbdZJiGIPV9xjUjabrjEauRDai.1
Meeting ID: 989 7755 9622
Passcode: 307452

LLM agents are revolutionizing complex task-solving through multi-step planning, reasoning, and real-world or simulated interactions. However, their adaptability to unseen tasks and environments remains a challenge, especially with limited training resources. In this talk, I will first introduce Agent Lumos (ACL 2024), a foundational framework for training general-purpose, open-source LLM agents that enables better generalization across domains, by the unified training over the trajectories converted from the ubiquitous, unstructured annotated reasoning rationales. I will also discuss Trial and Error (ACL 2024) and Q* Agent, which foster self-exploration, and collect trajectories for preference optimization and process reward modeling based on environmental feedback. Finally, I will outline future directions, including agent critique and world models, to enhance LLM adaptability with minimal effort.

Speaker Bio

Da Yin is a final-year PhD student in Computer Science at UCLA, advised by Prof. Kai-Wei Chang, working in the UCLA NLP lab. He was awarded Amazon PhD Fellowship and Best Paper Award at EMNLP Pan-DL workshop in 2023. He was also the co-organizer of 1st ACL MML workshop, publicity chair of 4th SocalNLP Symposium, and area chair at ACL ARR from 2023. His research interest is building generalizable, adaptive, and inclusive language processing models that can be applied across applications and regions.

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