9 citations · 9 across the 9 of their papers we have counts for
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cs.CL2024
Cost-Effective Hallucination Detection for LLMs
Simon Valentin, Jinmiao Fu, Gianluca Detommaso +3
Large language models (LLMs) can be prone to hallucinations - generating unreliable outputs that are unfaithful to their inputs, external facts or internally inconsistent. In this…
cs.CL2024
Q-Tuning: Queue-based Prompt Tuning for Lifelong Few-shot Language Learning
Yanhui Guo, Shaoyuan Xu, Jinmiao Fu +3
This paper introduces \textbf{Q-tuning}, a novel approach for continual prompt tuning that enables the lifelong learning of a pre-trained language model. When learning a new task,…