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lupantech/ScienceQA

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ScienceQA

Data and code for NeurIPS 2022 Paper "Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering".

Features

  • Chain Of Thought - Multimodal reasoning framework for science question answering.
  • Multimodal Datasets - Large-scale multi-choice multimodal reasoning dataset.

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ScienceQA 的开源替代方案

相似的开源项目,按与 ScienceQA 的功能重合度排序。
  • zeroqiaoba/explainable-multimodal-emotion-reasoningzeroQiaoba 的头像

    zeroQiaoba/Explainable-Multimodal-Emotion-Reasoning

    396在 GitHub 上查看↗

    EMER, OV-MER (ICML25), AffectGPT (ICML25, Oral), EmoPrefer (ICLR26)

    Python
    在 GitHub 上查看↗396
  • phodal/prompt-patternsphodal 的头像

    phodal/prompt-patterns

    3,096在 GitHub 上查看↗

    Prompt patterns is a framework for organizing AI-driven system design through structured prompt engineering and domain-driven development methodologies. It provides a library of standardized interaction strategies designed to improve the consistency, accuracy, and logical reasoning of large language model outputs. By applying these patterns, users can translate complex business scenarios into structured domain models and technical specifications. The project distinguishes itself by integrating domain-driven design principles directly into the prompting workflow. It utilizes techniques such as

    chatgptgithub-copilotprompt-engineering
    在 GitHub 上查看↗3,096
  • om-ai-lab/vlm-r1om-ai-lab 的头像

    om-ai-lab/VLM-R1

    5,991在 GitHub 上查看↗

    VLM-R1 is a reasoning vision-language model and embodied AI framework designed to map visual inputs and language instructions into physical navigation waypoints and robotic actions. It functions as a multimodal policy optimizer and an open vocabulary detector capable of locating objects based on arbitrary natural language descriptions. The system distinguishes itself through the use of chain-of-thought reasoning and reinforcement learning to solve complex visual and spatial tasks. It utilizes a video semantic memory system, which employs a visual cache to maintain a history of live video for

    Python
    在 GitHub 上查看↗5,991
  • pandabearlab/prompt-tutorialPandaBearLab 的头像

    PandaBearLab/prompt-tutorial

    1,330在 GitHub 上查看↗

    This project serves as an educational resource and guide for prompt engineering, providing a structured methodology for interacting with large language models. It focuses on teaching core strategies to improve the reliability, accuracy, and consistency of model outputs across a variety of natural language processing tasks. The framework emphasizes the use of standardized templates and logical decomposition to manage complex instructions. By implementing techniques such as few-shot context injection, iterative refinement, and delimiter-based segmentation, the project demonstrates how to guide

    在 GitHub 上查看↗1,330
查看 ScienceQA 的所有 19 个替代方案→

常见问题解答

lupantech/scienceqa 是做什么的?

Data and code for NeurIPS 2022 Paper "Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering".

lupantech/scienceqa 的主要功能有哪些?

lupantech/scienceqa 的主要功能包括:Chain Of Thought, Multimodal Datasets。

lupantech/scienceqa 有哪些开源替代品?

lupantech/scienceqa 的开源替代品包括: zeroqiaoba/explainable-multimodal-emotion-reasoning — EMER, OV-MER (ICML25), AffectGPT (ICML25, Oral), EmoPrefer (ICLR26). om-ai-lab/vlm-r1 — VLM-R1 is a reasoning vision-language model and embodied AI framework designed to map visual inputs and language… phodal/prompt-patterns — Prompt patterns is a framework for organizing AI-driven system design through structured prompt engineering and… qwenlm/qwen2-vl — Qwen2-VL is a multimodal large language model and vision language model designed to process and reason across text,… jacoblee93/fully-local-pdf-chatbot — This project is a private document analysis tool that enables conversational interaction with PDF files by executing… pandabearlab/prompt-tutorial — This project serves as an educational resource and guide for prompt engineering, providing a structured methodology…