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cs.IR2025
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…
cs.CL2025
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…