Chain-of-Interactions (CoI) introduces a novel multi-step framework that leverages LLMs’ in-context learning capabilities for abstractive task-oriented dialogue summarization. Through comprehensive evaluation across 11 models and human studies with 480 evaluators, CoI demonstrates decisive superiority with 6× better entity preservation and 49% higher quality scores compared to existing approaches, establishing new standards for customer service dialogue summarization.

GAMIC introduces a novel self-supervised learning approach for molecular in-context learning that combines graph neural networks with Morgan fingerprints to better capture molecular complexity. By aligning global molecular structures with textual descriptions and using Maximum Marginal Relevance for demonstration selection, GAMIC outperforms traditional Morgan-based retrieval methods by up to 45% across diverse molecular tasks.

Beemo introduces a novel benchmark featuring 6.5k expert-edited machine-generated texts across diverse domains from creative writing to summarization. Through comprehensive evaluation of 33 MGT detector configurations, we demonstrate that expert editing effectively evades detection while LLM-edited texts remain distinguishable from human writing, highlighting critical gaps in current detection methods for multi-author scenarios.

This IEEE Intelligent Systems article examines the “longtail” impact of Generative AI on disinformation in high-impact events and resource-limited settings. We analyze four critical dimensions—quantity, quality, personalization, and hallucination—proposing strategies to combat disinformation risks in vulnerable communities and during critical events where consequences are most profound.

This research project is a collaboration with Penn State and MIT Lincoln Lab. Our study demonstrates the dual capacity of LLMs for offensive misuse and defense detection against disinformation without requiring additional training.