Apart from the already-large model parameters, the key/value (KV) cache that holds information about previous tokens in a sequence can grow to be even larger than the model itself. This problem is exacerbated in one of the current LLM serving frameworks which reserves the maximum sequence length of memory for the KV cache to guarantee generating a complete sequence as they do not know the output sequence length. This restricts us to use a smaller batch size leading to lower GPU utilization and above all, lower throughput. We argue that designing a system with a priori knowledge of the output sequence can mitigate this problem.

Multivariate time-series forecasting plays a crucial role in many real-world applications.

Multivariate time-series forecasting plays a crucial role in many real-world applications.

One reason is that the notion of interpretability is notoriously challenging to pin down, andtherefore existing CBMs rely ondifferent heuristics--such asencouraging theconcepts tobe sparse [1], orthonormal to each other [5], or match the contents of concrete examples [3]--with unclear properties and incompatible goals.

InIRsystems, theretrievermodule directly influences theperformance ofdownstream tasks, such as retrieval-augmented generation [20, 29, 30] and knowledge-intensive question answering [34, 52]. However, these methods do not explicitly consider targeted optimization for tool usage or the impact on complex multi-stage tasks.

Markov chain Monte Carlo (MCMC) is an inference mechanism that approximates a target probability distribution by a sequence of states (a.k.a.

The first two permit using subgradients andstochastic gradients, andareshowntoensureaO(1/ t) convergencerate.

The first two permit using subgradients andstochastic gradients, andareshowntoensureaO(1/ t) convergencerate.

Language models have been shown to be very effective in predicting brain recordings of subjects experiencing complex language stimuli.