Agent-Based Models (ABMs) are used in several fields to study the evolution of complex systems from micro-level assumptions. However, a significant drawback of ABMs is their inability to estimate agent-specific (or “micro”) variables, which hinders the
To optimize the search process, mathematical models of lost person behavior with respect to landscape can be used in conjunction with current SAR practices. In this paper, we introduce an agent-based model of lost person behavior which allows agents to move on known landscapes with behavior ...
Agent-based models tend to seek explanatory insight into the collective behavior of agents (often real-world organisms or systems), whereas multi-agent systems tend to be more focused on solving practical or engineering problems through optimization of the design of agents. Insights from this topic...
🔥🔥🔥Woodpecker: Hallucination Correction for Multimodal Large Language Models Paper|GitHub This is the first work to correct hallucination in multimodal large language models. ✨ Table of Contents Awesome Papers Multimodal Instruction Tuning Multimodal Hallucination Multimodal In-Context Learning Multim...
[1] Yujia Qin, Shengding Hu, Yankai Lin, Weize Chen, Ning Ding, Ganqu Cui, Zheni Zeng, YufeiHuang, Chaojun Xiao, Chi Han, et al. Tool learning with foundation models. arXiv preprintarXiv:2304.08354, 2023. 总结了使大语言模型使用外部扩展工具的先进方法。
On average, models trained in a joint learning mode are more likely to label short utterances. Table 4. Quantitative summarization results on the SAMSum and DialogSum test sets after fine-tuning (3-runs average). Top: abstractive baselines. Bottom: backbone vanilla models and our annotate-then-...
Industrial automation: In the field of industrial automation, LLM-based agents can be used to achieve intelligent planning and control of production processes. [129] proposes a novel framework that integrates large language models (LLMs) with digital twin systems to accommodate flexible production need...
Maxim has been working as a software developer for more than 20 years and was involved in various areas: distributed scientific computing, distributed systems and big data processing. Since 2014 he is actively using machine and deep learning to solve practical industrial tasks, such as NLP problems...
Grokking: Learning Is Generalization and Not Memorization -- Understanding how a neural network learns helps us to avoid that the model from forgetting what it learns A fAIry tale of the Inductive Bias -- Do we need inductive bias? How simple models can reach the performance of complex models...
Machine Learning for NLP A Comprehensive Survey on Word Representation Models: From Classical to State-Of-The-Art Word Representation Language Models. ACM Trans. Asian Low Resour. Lang. Inf. Process. 2021 paper bib Usman Naseem, Imran Razzak, Shah Khalid Khan, Mukesh Prasad A Survey Of Cross...