A representative evaluation benchmark for MLLMs. ✨ 🔥🔥🔥Woodpecker: Hallucination Correction for Multimodal Large Language Models Paper|GitHub This is the first work to correct hallucination in multimodal large language models. ✨ 🔥🔥🔥Freeze-Omni: A Smart and Low Latency Speech-to-s...
人类大脑作为一个复杂的信息处理系统,首先将感觉信息转化为知觉表征,然后利用这些表征构建对世界的知识并做出决策,最后通过行动来实施决策。这种抽象的序列反映了游戏代理中典型的迭代周期,即perception , inference , and action,因此我们在本次综述中采用了类似的组织结构。图1展示了核心综述结构,涵盖了感觉信息如何转化...
Especially when actions are permutations, vector-based decision spaces get very large, very quickly. Enumerable: An action space of a billion may be ‘large’ by conventional standards; you definitely don’t want to evaluate a billion actions for every iteration. Such an action space can still...
Learning/acquiring symbolic domain models 利用LLM蕴含的大量知识,将LLM建立为world model或者一个plan critic;但是有证据显示这种model缺乏可靠的(对action effects的)reasoning,在候选的plan中容易发生错误 Language models with access to external tools 例如让LLM调用外部的数学或者逻辑归因的工具,这里作者是调用了外部...
A Survey of Large Language Models; Wayne Xin Zhao et al Tool Learning with Foundation Models; Yujia Qin et al A Cookbook of Self-Supervised Learning; Randall Balestriero et al Foundation Models for Decision Making: Problems, Methods, and Opportunities; Sherry Yang et al Bridging the Gap: A ...
. Evaluation and mitigation of the limitations of large language models in clinical decision-making. Nat Med2024;30:2613-22. doi:10.1038/s41591-024-03097-1 pmid:38965432 OpenUrlCrossRefPubMedGoogle Scholar ↵ Menz BD, Kuderer NM, Bacchi S, et al. Current safeguards, risk mitigation, and ...
The recent rise of large language models (LLMs), such as generative pre-trained transformer (GPT) models, has shown some promise that artificial theory of mind may not be too distant an idea. Generative LLMs exhibit performance that is characteristic of sophisticated decision-making and reasoning...
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Extending the Planner’s action space to leverage reaction databases, such as Reaxys32or SciFinder33, should significantly enhance the system’s performance (especially for multistep syntheses). Alternatively, analysing the system’s previous statements is another approach to improving its accuracy. This...
Token-level decision:逐步地,一点一点,基于附近的信息,和基于临近的contact,以无意识的方式去生成的,缺乏前后的反复审查 但是我们日常的写作中,是需要不断去调整我们写作的逻辑,或者我们需要去改我们的story,有时也会需要推翻重来 大语言模型并没有这样的功能 ...