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KAG is a logical form-guided reasoning and retrieval framework based on OpenSPG engine and LLMs. It is used to build logical reasoning and factual Q&A solutions for professional domain knowledge bases. It can effectively overcome the shortcomings of the traditional RAG vector similarity calculation model.
Recent Papers including Neural Symbolic Reasoning, Logical Reasoning, Visual Reasoning, planning and any other topics connecting deep learning and reasoning
[EMNLP 2024] This is the official implementation of the paper "A Peek into Token Bias: Large Language Models Are Not Yet Genuine Reasoners" in PyTorch.
The source code for Abstract Meaning Representation-Based Logic-Driven Data Augmentation for Logical Reasoning. #1 on the ReClor Leaderboard and we are the first group scored above 90% on the hidden test set around the world.. The paper has been accepted by the Findings of ACL-24.
An AI for playing Minesweeper, utilizing propositional logic and knowledge-based inference to identify safe cells and mines. The AI learns from the game's state to make informed decisions.