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Agiollo, A. (author), Cavalcante Siebert, L. (author), Murukannaiah, P.K. (author), Omicini, Andrea (author)
Although popular and effective, large language models (LLM) are characterised by a performance vs. transparency trade-off that hinders their applicability to sensitive scenarios. This is the main reason behind many approaches focusing on local post-hoc explanations recently proposed by the XAI community. However, to the best of our knowledge,...
conference paper 2023
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van der Meer, M.T. (author), Liscio, E. (author), Jonker, C.M. (author), Plaat, Aske (author), Vossen, Piek (author), Murukannaiah, P.K. (author)
The key arguments underlying a large and noisy set of opinions help understand the opinions quickly and accurately. Fully automated methods can extract arguments but (1) require large labeled datasets and (2) work well for known viewpoints, but not for novel points of view. We propose HyEnA, a hybrid (human + AI) method for extracting...
conference paper 2022
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Liscio, E. (author), van der Meer, M.T. (author), Jonker, C.M. (author), Murukannaiah, P.K. (author)
Value alignment is a crucial aspect of ethical multiagent systems. An important step toward value alignment is identifying values specific to an application context. However, identifying contextspecific values is complex and cognitively demanding. To support this process, we develop a methodology and a collaborative web platform that employs AI...
conference paper 2021