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https://arxiv.org/html/2608.03411v1
com
Abstract
Accurate Uncertainty Quantification (UQ) is critical for reliable deployment of Large Language Models (LLMs), yet traditional probability-based metrics often fail to capture the model’s true epistemic state
https://arxiv.org/html/2608.02625v1
Despite showing early promise, especially in terms of efficiency, DLMs face a fundamental challenge when applied to language generation: DLMs are by design inherently bidirectional
https://arxiv.org/html/2410.05779v3
com Both Chao Huang and Yanhua Yu are corresponding authors
https://arxiv.org/abs/1611.09268
The dataset comprises of 1,010,916 anonymized questions---sampled from Bing's search query logs---each with a human generated answer and 182,669 completely human rewritten generated answers
https://arxiv.org/html/2608.03573v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/list/cs.DB/new
Where the consumer cannot inspect the generated query, as in enterprise AI deployments and operational dashboards, and increasingly where the consumer is a tool-using agent rather than a person, accuracy alone is insufficient: nothing marks which answers to distrust
https://arxiv.org/html/2504.19413v1
2 Proposed Methods
2.1 Mem0
2.2 Mem0gsuperscriptMem0𝑔\texttt{Mem0}^{\tiny g}Mem0 start_POSTSUPERSCRIPT italic_g end_POSTSUPERSCRIPT
3 Experimental Setup
3.1 Dataset
3.2 Evaluation Metrics
(1) Performance Metrics
(2) Deployment Metrics