5 papers · 1 filter
Decomposed Entailment for Factuality Checking and Hallucination Detection
Achir Oukelmoun, Nasredine Semmar, Gaël De Chalendar
The reliability of Large Language Models (LLMs) is often compromised by factual inconsistencies, including hallucinations---cases where generated content is not supported by the un…
How DDAIR you? Disambiguated Data Augmentation for Intent Recognition
Galo Castillo-López, Alexis Lombard, Nasredine Semmar +1
Large Language Models (LLMs) are effective for data augmentation in classification tasks like intent detection. In some cases, they inadvertently produce examples that are ambiguou…
Intent Recognition and Out-of-Scope Detection using LLMs in Multi-party Conversations
Galo Castillo-López, Gaël de Chalendar, Nasredine Semmar
Intent recognition is a fundamental component in task-oriented dialogue systems (TODS). Determining user intents and detecting whether an intent is Out-of-Scope (OOS) is crucial fo…
The Structure-Content Trade-off in Knowledge Graph Retrieval
Valentin Six, Evan Dufraisse, Gaël de Chalendar
Large Language Models (LLMs) increasingly rely on knowledge graphs for factual reasoning, yet how retrieval design shapes their performance remains unclear. We examine how question…
Benchmarks Underestimate the Readiness of Multi-lingual Dialogue Agents
Andrew H. Lee, Sina J. Semnani, Galo Castillo-López +16
Creating multilingual task-oriented dialogue (TOD) agents is challenging due to the high cost of training data acquisition. Following the research trend of improving training data…