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pro vyhledávání: '"Vennam, Sreeram"'
Most existing Question Answering Datasets (QuADs) primarily focus on factoid-based short-context Question Answering (QA) in high-resource languages. However, the scope of such datasets for low-resource languages remains limited, with only a few works
Externí odkaz:
http://arxiv.org/abs/2408.10604
Graph Neural Networks (GNNs) have emerged as powerful tools for learning representations of graph-structured data, demonstrating remarkable performance across various tasks. Recognising their importance, there has been extensive research focused on e
Externí odkaz:
http://arxiv.org/abs/2406.03253
Autor:
Bonagiri, Vamshi Krishna, Vennam, Sreeram, Govil, Priyanshul, Kumaraguru, Ponnurangam, Gaur, Manas
Despite recent advancements showcasing the impressive capabilities of Large Language Models (LLMs) in conversational systems, we show that even state-of-the-art LLMs are morally inconsistent in their generations, questioning their reliability (and tr
Externí odkaz:
http://arxiv.org/abs/2402.13709
A Large Language Model (LLM) is considered consistent if semantically equivalent prompts produce semantically equivalent responses. Despite recent advancements showcasing the impressive capabilities of LLMs in conversational systems, we show that eve
Externí odkaz:
http://arxiv.org/abs/2402.01719