基本信息论文《Large Language Models Are Neurosymbolic Reasoners》探讨了大型语言模型(LLMs)作为符号推理器的潜在应用。ArXiv发布于2024/01/17。 本文是论文主要内容的翻译,一切请以原文为准。论文地址: La…
2024 EMNLP Large Language Models are Limited in Out-of-Context Knowledge Reasoning阅读笔记 维克多 在读硕士,研究LLM知识,推理和跨语言7 人赞同了该文章 简介 LLMs具有丰富的知识以及具备强大的上下文推理能力,但其利用训练数据中的知识进行推理(out-of-context reasoning)的能力仍受质疑。 本论文研究了out-...
综上,为了减少创建和验证有效指令(instructions)所涉及的人力和困难度,我们提出了使用LLMs自动生成和选择指令(instructions)的新算法,这本质属于“自然语言合成(natural language program synthesis)”的理论范畴,算法采用黑盒优化的方法,基于LLM来自动生成,并启发式地搜索可行的候选解决方案。 这个算法能够生效,需要建立在...
文章要点:文章提出一个evolvable LLM-based agent框架REMEMBERER,主要思路是给大模型加一个experience memory存储过去的经验,然后用Q-learning的方式计算Q值,再根据任务相似度采样轨迹和对应的Q值作为prompt指导LLM进一步选取动作和环境交互。这里的Semi-Parametric Reinforcement Learning就指的experience memory可以用RL来计算Q值...
Large language models (LLMs) are deep learning algorithms that can recognize, summarize, translate, predict, and generate content using very large datasets.
Large Language Models are Competitive Near Cold-start Recommenders for Language- and Item-based Preferences O网页链接ChatPaper综述:文章研究了使用大型语言模型(LLM)根据基于物品和语言的偏好进行推荐的效果,并与最先进的基于物品的协同过滤(CF)方法进行比较。研究人员收集了一种新的数据集,其中包含用户基于物品和...
the TitanFuzz work demonstrates that modern Large Language Models (LLMs) can be directly leveraged to implicitly learn all the constraints to generate valid DL programs for fuzzing. However, LLMs tend to generate ordinary programs following similar patterns seen in their massive training corpora, whi...
Large language models (LLMs) are a category of foundation models trained on immense amounts of data making them capable of understanding and generating natural language and other types of content to perform a wide range of tasks. LLMs have become a household name thanks to the role they have...
Large language models are better than theoretical linguists at theoretical linguistics, at least in the domain of verb argument structure; explaining why (for example), we can say both The ball rolled and Someone rolled the ball, but not both The man laughed and *Someone laughed the man. ...
LanguageModelsareUnsupervisedMultitaskLearnersAlecRadford*1JeffreyWu*1RewonChild1DavidLuan1DarioAmodei**1IlyaSutskever**1AbstractNaturallanguageprocessingtasks,suchasques-tionanswering,machinetranslation,readingcom-prehension,andsummarization,aretypicallyapproachedwithsupervisedlearningontask-specificdatasets.Wedemonstr...