lmm

LMM is an acronym that can refer to several concepts in statistics and artificial intelligence. The two most common uses are Linear Mixed Models and Large Multimodal Models.

Linear Mixed Models are statistical models that extend linear regression to data with correlated or grouped observations. They include fixed effects, which apply to the entire population, and random effects, which capture variation attributable to groups or units such as subjects or sites. A typical formulation is y = Xβ + Zb + ε, where β are fixed coefficients, b are random effects with b ~ N(0, G), and ε ~ N(0, R). They accommodate unbalanced data and repeated measurements and can model hierarchical structures. Estimation is commonly performed via maximum likelihood or restricted maximum likelihood (REML). Applications span fields such as agriculture, psychology, ecology, and econometrics. Model specification involves choosing which effects to treat as fixed or random and selecting covariance structures.

Large Multimodal Models are AI systems designed to process and reason over multiple data modalities, such as text, images, audio, or video, often by combining a multimodal encoder with a language model. They differ from purely text-based large language models by incorporating components that handle non-text inputs and by fusing information across modalities. Training typically relies on large multimodal datasets and may involve pretraining of encoders, alignment objectives, and multimodal fine-tuning. LMMs aim to support tasks such as image captioning, visual reasoning, and multimodal question answering. Challenges include data alignment, potential hallucination, and considerations around safety and evaluation across modalities.

Because LMM is an acronym with several meanings, the intended interpretation depends on the context.