15 citations · 17 across the 10 of their papers we have counts for
10 papers
Learning When to Retrieve, What to Rewrite, and How to Respond in Conversational QA
Nirmal Roy, Leonardo F. R. Ribeiro, Rexhina Blloshmi +1
Augmenting Large Language Models (LLMs) with information retrieval capabilities (i.e., Retrieval-Augmented Generation (RAG)) has proven beneficial for knowledge-intensive tasks. Ho…
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…
Incorporating Relevance Feedback for Information-Seeking Retrieval using Few-Shot Document Re-Ranking
Tim Baumgärtner, Leonardo F. R. Ribeiro, Nils Reimers +1
Pairing a lexical retriever with a neural re-ranking model has set state-of-the-art performance on large-scale information retrieval datasets. This pipeline covers scenarios like q…
UKP-SQUARE: An Online Platform for Question Answering Research
Tim Baumgärtner, Kexin Wang, Rachneet Sachdeva +10
Recent advances in NLP and information retrieval have given rise to a diverse set of question answering tasks that are of different formats (e.g., extractive, abstractive), require…
Smelting Gold and Silver for Improved Multilingual AMR-to-Text Generation
Leonardo F. R. Ribeiro, Jonas Pfeiffer, Yue Zhang +1
Recent work on multilingual AMR-to-text generation has exclusively focused on data augmentation strategies that utilize silver AMR. However, this assumes a high quality of generate…
Structural Adapters in Pretrained Language Models for AMR-to-text Generation
Leonardo F. R. Ribeiro, Yue Zhang, Iryna Gurevych
Pretrained language models (PLM) have recently advanced graph-to-text generation, where the input graph is linearized into a sequence and fed into the PLM to obtain its representat…