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Large Language Models (LLMs) have emerged as highly capable systems and are increasingly being integrated into various uses. Nevertheless, t… (voir plus)he rapid advancement in their deployment trails a comprehensive understanding of their internal mechanisms, as well as a delineation of their capabilities and limitations. A desired characteristic of an intelligent system is its ability to recognize the scope of its own knowledge. To investigate whether LLMs embody this attribute, we develop a benchmark that challenges these models to enumerate all information they possess on specific topics. This benchmark assesses whether the models recall excessive, insufficient, or the precise amount of required information, thereby indicating their awareness of how much they know about the given topic. Our findings reveal that the emergence of this property varies across different architectures and manifests at diverse rates. However, with sufficient scaling, all tested models are ultimately capable of performing this task. The insights gained from this research advance our understanding of LLMs, shedding light on their operational capabilities and contributing to the ongoing exploration of their intricate dynamics.
2024-01-01
Conference on Empirical Methods in Natural Language Processing (publié)
Navigating through the exponentially large chemical space to search for desirable materials is an extremely challenging task in material dis… (voir plus)covery. Recent developments in generative and geometric deep learning have shown...
Navigating through the exponentially large chemical space to search for desirable materials is an extremely challenging task in material dis… (voir plus)covery. Recent developments in generative and geometric deep learning have shown...
In the last years, there has been a great interest in machine‐learning‐based heuristics for solving NP‐hard combinatorial optimization… (voir plus) problems. The developed methods have shown potential on many optimization problems. In this paper, we present a learned heuristic for the reoptimization of a problem after a minor change in its data. We focus on the case of the capacited vehicle routing problem with static clients (i.e., same client locations) and changed demands. Given the edges of an original solution, the goal is to predict and fix the ones that have a high chance of remaining in an optimal solution after a change of client demands. This partial prediction of the solution reduces the complexity of the problem and speeds up its resolution, while yielding a good quality solution. The proposed approach resulted in solutions with an optimality gap ranging from 0% to 1.7% on different benchmark instances within a reasonable computing time.
Learning Tabu Search Algorithms: A Scheduling Application
Nazgol Niroumandrad
Nadia Lahrichi
Andrea Lodi
. Metaheuristics are widely recognized as efficient approaches for many combinatorial problems. Studies to improve the performance of metahe… (voir plus)uristics have increasingly relied on the use of various methods either combining different metaheuristics or methods originating outside of the metaheuristic field. This paper presents a learning algorithm to improve tabu search by reducing its search space and the evaluation effort. We study the performance of a learning tabu search algorithm using classification methods in an attempt to select moves through the search space more wisely. The experimental results demonstrate the benefit of using a learning mechanism under deterministic and stochastic conditions.
Reinforcement learning is an attractive approach to learn good resource allocation and scheduling policies based on data when the system mod… (voir plus)el is unknown. However, the cumulative regret of most RL algorithms scales as ˜ O(S
2024-01-01
IEEE Transactions on Control of Network Systems (publié)
