Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Huebscher, Michelle Chen"'
Autor:
Bulian, Jannis, Schäfer, Mike S., Amini, Afra, Lam, Heidi, Ciaramita, Massimiliano, Gaiarin, Ben, Hübscher, Michelle Chen, Buck, Christian, Mede, Niels G., Leippold, Markus, Strauß, Nadine
Publikováno v:
Proceedings of the 41st International Conference on Machine Learning (ICML), 2024
As Large Language Models (LLMs) rise in popularity, it is necessary to assess their capability in critically relevant domains. We present a comprehensive evaluation framework, grounded in science communication research, to assess LLM responses to que
Externí odkaz:
http://arxiv.org/abs/2310.02932
Autor:
Adolphs, Leonard, Huebscher, Michelle Chen, Buck, Christian, Girgin, Sertan, Bachem, Olivier, Ciaramita, Massimiliano, Hofmann, Thomas
Neural retrieval models have superseded classic bag-of-words methods such as BM25 as the retrieval framework of choice. However, neural systems lack the interpretability of bag-of-words models; it is not trivial to connect a query change to a change
Externí odkaz:
http://arxiv.org/abs/2210.12084
Learning to search is the task of building artificial agents that learn to autonomously use a search box to find information. So far, it has been shown that current language models can learn symbolic query reformulation policies, in combination with
Externí odkaz:
http://arxiv.org/abs/2209.15469
Autor:
Adolphs, Leonard, Boerschinger, Benjamin, Buck, Christian, Huebscher, Michelle Chen, Ciaramita, Massimiliano, Espeholt, Lasse, Hofmann, Thomas, Kilcher, Yannic, Rothe, Sascha, Sessa, Pier Giuseppe, Saralegui, Lierni Sestorain
This paper presents first successful steps in designing search agents that learn meta-strategies for iterative query refinement in information-seeking tasks. Our approach uses machine reading to guide the selection of refinement terms from aggregated
Externí odkaz:
http://arxiv.org/abs/2109.00527
Autor:
Borschinger, Benjamin, Boyd-Graber, Jordan, Buck, Christian, Bulian, Jannis, Ciaramita, Massimiliano, Huebscher, Michelle Chen, Gajewski, Wojciech, Kilcher, Yannic, Nogueira, Rodrigo, Saralegu, Lierni Sestorain
We investigate a framework for machine reading, inspired by real world information-seeking problems, where a meta question answering system interacts with a black box environment. The environment encapsulates a competitive machine reader based on BER
Externí odkaz:
http://arxiv.org/abs/1911.04156