5 papers
Improving Document Retrieval Coherence for Semantically Equivalent Queries
Stefano Campese, Alessandro Moschitti, Ivano Lauriola
Dense Retrieval (DR) models have proven to be effective for Document Retrieval and Information Grounding tasks. Usually, these models are trained and optimized for improving the re…
Tuning-Free Personalized Alignment via Trial-Error-Explain In-Context Learning
Hyundong Cho, Karishma Sharma, Nicolaas Jedema +4
Language models are aligned to the collective voice of many, resulting in generic outputs that do not align with specific users' styles. In this work, we present Trial-Error-Explai…
Speechworthy Instruction-tuned Language Models
Hyundong Cho, Nicolaas Jedema, Leonardo F. R. Ribeiro +5
Current instruction-tuned language models are exclusively trained with textual preference data and thus are often not aligned with the unique requirements of other modalities, such…
Datasets for Multilingual Answer Sentence Selection
Matteo Gabburo, Stefano Campese, Federico Agostini +1
Answer Sentence Selection (AS2) is a critical task for designing effective retrieval-based Question Answering (QA) systems. Most advancements in AS2 focus on English due to the sca…
Measuring Retrieval Complexity in Question Answering Systems
Matteo Gabburo, Nicolaas Paul Jedema, Siddhant Garg +2
In this paper, we investigate which questions are challenging for retrieval-based Question Answering (QA). We (i) propose retrieval complexity (RC), a novel metric conditioned on t…