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cs.CL2025
Eliciting Reasoning in Language Models with Cognitive Tools
Brown Ebouky, Andrea Bartezzaghi, Mattia Rigotti
The recent advent of reasoning models like OpenAI's o1 was met with excited speculation by the AI community about the mechanisms underlying these capabilities in closed models, fol…
cs.CL2024
Combining Data Generation and Active Learning for Low-Resource Question Answering
Maximilian Kimmich, Andrea Bartezzaghi, Jasmina Bogojeska +2
Neural approaches have become very popular in Question Answering (QA), however, they require a large amount of annotated data. In this work, we propose a novel approach that combin…
cs.CL2024
Prompting-based Synthetic Data Generation for Few-Shot Question Answering
Maximilian Schmidt, Andrea Bartezzaghi, Ngoc Thang Vu
Although language models (LMs) have boosted the performance of Question Answering, they still need plenty of data. Data annotation, in contrast, is a time-consuming process. This e…