LLMs for Actual Causality and Causal Judgments 反事实推理 推断必要和充分原因 推断是否符合社会规范 Conclusion Abstract 探索大型语言模型(LLMs)在医学、科学、法律和政策等具有社会影响力的领域的因果能力。 在这项研究中,基于大语言模型的方法在多个因果基准测试任务上表现出最高的准确性。基于GPT-3.5/4的算法在...
However, with the emergence of various knowledge graphs and large language models, causal inference tailored to knowledge graphs and large models has gradually become a research hotspot. In this paper, different causal inference methods are classified based on their orienta...
CasualNet is poised to serve as a benchmark for future advancements in this field, providing a rigorous testing ground for emerging AI models. 此外,本文提出一个新的因果评测数据集CausalNet。CausalNet作为新基准给模型提供了严格的测试环境。 文章链接 Cause and Effect: Can Large Language Models Truly...
Reports of Large Language Models (LLMs) passing board examinations have spurred medical enthusiasm for their clinical integration. Through a narrative review, we reflect upon the skill shifts necessary for clinicians to succeed in an LLM-enabled world, achieving benefits while minimizing risks. We sug...
Causal inference with large language models (LLMs) is an important research topic with various applications. In this study, we examine whether metacognitive prompting, which has recently been reported to be effective for other tasks and promotes deeper insight into LLMs, improves causal inference in...
large language models, particularly those designed for visual processing, have rekindled interest in the potential to emulate human-like cognitive abilities. This paper evaluates the current state of vision-based large language models in the domains of intuitive physics, causal reasoning and intuitive ...
Describe, explain, plan and select: interactive planning with large language models enables open-world multi-task agents. 2023, arXiv preprint arXiv: 2302.01560 Lin J, Zhao H, Zhang A, Wu Y, Ping H, Chen Q. AgentSims: an open-source sandbox for large language model evaluation. 2023, ar...
[arxiv] In-Context Learning for Knowledge Base Question Answering for Unmanned Systems based on Large Language Models.2023.11 [arxiv] Let's Discover More API Relations: A Large Language Model-based AI Chain for Unsupervised API Relation Inference.2023.11 ...
Large Language Models and Causal Inference in Collaboration: A Comprehensive Survey, X. Liu et al, U of Maryland College Park, 2024 Unfamiliar Finetuning Examples Control How Language Models Hallucinate, Katie Kang et al, 2024 Demystifying Embedding Spaces using Large Language Models, G. Tennen...
Efficient Causal Graph Discovery Using Large Language Modelsarxiv.org/abs/2402.01207 方法简介 方法动机 Kıcıman et al. (2023); Choi et al. (2022); Long et al. (2023b) use pairwise queries to infer the causal relationship between 2 variables at a time. 现有的方法使用配对查询的方...