Large Language Models fail at implicit multi-hop reasoning: a model answers"When was $X$ born?"and"Who is $Y$'s closest friend?"correctly but fails on"When was $Y$'s closest friend born?"in a single forward pass, even when both facts are perfectly memorized and individually retrievable. We study this failure in a contr...
Yannis Karmim, Luis Martí, Djamé Seddah et al.· arXiv.org· 0 citations
G-Retriever is competitive with RAG and reduces the error of the base LLM by 72\% with a standard KG and 78\% with a benchmark-aware variant, the gap to RAG narrowing further as the graph is oriented toward task-relevant content.
Pablo Poulenard, Yannis Karmim, Valentin Barrière· 0 citations
This paper presents a simple method that allows to easily enhance textual pre-trained large language models with speech information, when fine-tuned for a specific classification task, and demonstrates its effectiveness on Argumentative Fallacy Detection and Classification tasks, and affective computing tasks on a wide...
Nicolas Calbucura, Valentin Barrière· arXiv.org· 1 citation
This work investigates whether large language models (LLMs) can automatically adapt template-based bias datasets to specific domains using zero-shot prompting and shows that domain-adapted templates capture real-world bias patterns more faithfully than standard templates.
Tamara Quiroga, Felipe Bravo-Marquez, Valentin Barrière· ACM Transactions on Intellig...· 0 citations
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