paper-conference

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 work introduces semantic captioning for SQL queries, addressing the reverse operation of semantic parsing by translating SQL code into natural language explanations. Using graph-aware few-shot in-context learning with smaller LLMs, our approach outperforms random selection by up to 39% on BLEU score, providing crucial capabilities for understanding and explaining LLM-generated SQL code in security-critical applications.

This research from Penn State and KiNiT, benchmarks the effectiveness of 10 authorship obfuscation (AO) techniques against 37 machine-generated text (MGT) detection methods across 11 languages, totaling 4,070 evaluations. It reveals that all AO methods can evade detection in every language, particularly highlighting the efficacy of homoglyph attacks. This underscores the need for improved multilingual MGT detection strategies.

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.

This research from Penn State and KiNiT introduces MULTITuDE, a novel multilingual dataset for detecting machine-generated text. Comprised of over 74,000 authentic and artificially-generated texts in 11 languages from 8 models, MULTITuDE benchmarks text generation capabilities in non-English languages and multilingual detection performance. The dataset addresses current gaps in analyzing and systematically evaluating machine text generation and detection across multiple languages.

This paper is the first attempt to detect COVID-19 misinformation (in English, Spanish, and Haitian French) populated in the Caribbean regions, using the fact-checked claims in the US (in English).