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pro vyhledávání: '"Rajeev, Meghana Arakkal"'
Autor:
Ramamurthy, Rajkumar, Rajeev, Meghana Arakkal, Molenschot, Oliver, Zou, James, Rajani, Nazneen
Large language models (LLMs) often fail to synthesize information from their context to generate an accurate response. This renders them unreliable in knowledge intensive settings where reliability of the output is key. A critical component for relia
Externí odkaz:
http://arxiv.org/abs/2411.03300
Autor:
Trivedi, Prapti, Gulati, Aditya, Molenschot, Oliver, Rajeev, Meghana Arakkal, Ramamurthy, Rajkumar, Stevens, Keith, Chaudhery, Tanveesh Singh, Jambholkar, Jahnavi, Zou, James, Rajani, Nazneen
LLM-as-a-judge models have been used for evaluating both human and AI generated content, specifically by providing scores and rationales. Rationales, in addition to increasing transparency, help models learn to calibrate its judgments. Enhancing a mo
Externí odkaz:
http://arxiv.org/abs/2410.05495