在原来COT prompt里面,作者给了一些简单的reasoning step,去引导model生成reasoning step,最后得到final answer 提出可以讲reasoning step变得更加具体,更加细化,更加复杂,LLM也会学着生成step by step的reasoning 通过这种方法,可以取得很好的performance improvement 在探索explore上面 可以让LLM生成很多的rationale,从中选出...
3. 自我验证与打分 自验证模块利用LLM的溯因推理能力(Xu et al. 2023)来检查链式图推理和发散扩展模块动态生成的推理链的合理性和连贯性。其中,为这次自我验证设定了一个阈值分数来判断推理的合理性。这提高了链式图推理和发散扩展模块的动态构建推理链的整体鲁棒性,确保符合用户兴趣的推荐的可靠推理。 基本步骤: ...
Question Answering over KGs (KGQA) is the task of answering natural questions grounding the reasoning to the information provided by the KG. Large Language Models (LLMs) are the state-of-the-art models for QA tasks due to their remarkable ability to understand natural language. On the other ...
Unfortunately, the logical reasoning problem does not go away as language models become larger. It just becomes hidden it in their huge architecture and very large training corpus. LLMs can spit out facts and nicely stitched-together sentences, but when it comes to logical reasoning, they are s...
Summary of evaluation on natural language processing tasks: NLU(Natural Language Understanding, including SA(Sentiment Analysis), TC(Text Classification), NLI(Natural Language Inference) and other NLU tasks), Rng.(Reasoning), NLG(Natural Language Generation, including Summ.(Summarization), Dlg.(Dialogue...
Remarkably, LISA can handle cases involving complex reasoning and world knowledge. Also, it demonstrates robust zero-shot capability when trained exclusively on reasoning-free datasets. In addition, fine-tuning the model with merely 239 reasoning segmentation data samples results in further perform...
large language model (the text-davinci-003 variant of Generative Pre-trained Transformer (GPT)-3) on a range of analogical tasks, including a non-visual matrix reasoning task based on the rule structure of Raven’s Standard Progressive Matrices. We found that GPT-3 displayed a surprisingly ...
LISA: Reasoning Segmentation via Large Language Model Motivation & Abs 现有的感知系统依赖人类的指示,难以主动推理以理解人类意图。 新任务:reasoning segmentation,模型需要根据给定的复杂 / 具有隐含意义的文本输出相应的seg mask。 新的benchmark:包含1000张左右图像的数据集(image-instruction-mask)。
Large Language Model Cascades with Mixture of Thoughts Representations for Cost-efficient Reasoning Murong Yue, Jie Zhao, Min Zhang, Liang Du, Ziyu Yao ICLR 2024|October 2023 Publication 下载BibTex Large language models (LLMs) such as GPT-4 have exhibi...
思维链提示引发了大型语言模型中的推理能力(Chain-of-Thought Prompting Elicits Reasoning in Large Language Models),这是一篇Google在2022年发布的论文,引入了"思维链提示"(chain-of thought prompting),是现在各种LLM应用和Prompt Engineering的重要基础。