5 accepted papers
Efficiently editing knowledge stored in Large Language Models (LLMs) enables model updates without large-scale training. One promising solution is Locate-and-Edit (L\&E), allowing simultaneous modifications of a massive number of factual knowledge. However, such editing may compromise the general ab…
Although applications involving long-context inputs are crucial for the effective utilization of large language models (LLMs), they also result in increased computational costs and reduced performance. To address this challenge, we propose an efficient, training-free prompt compression method that r…
Transformer based Large Language Models (LLMs) often impose limitations on the length of the text input to ensure the generation of fluent and relevant responses due to the quadratic complexity. These constraints restrict their applicability in long text scenarios. In this paper, we propose a novel…
Prediction is a fundamental capability of all living organisms, and has been proposed as an objective for learning sensory representations. Recent work demonstrates that in primate visual systems, prediction is facilitated by neural representations that follow straighter temporal trajectories than…
Many real-world phenomena arise from causal relationships among a set of variables. As a powerful tool, Bayesian Network (BN) has been successful in describing high-dimensional distributions. However, the faithfulness condition, enforced in most BN learning algorithms, is violated in the settings wh…