Papers with Code 아카이브
Knowledge Graph Completion
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- 평가표
- 7
- 보존된 평가 기록
- 17
Knowledge graphs $G$ are represented as a collection of triples $\\{(h, r, t)\\}\subseteq E\times R\times E$, where $E$ and $R$ are the entity set and relation set. The task of Knowledge Graph Completion is to either predict unseen relations $r$ between two existing entities: $(h, ?, t)$ or predict the tail entity $t$ given the head entity and the query relation: $(h, r, ?)$.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/mianzg/kgbiasdetec
공식 코드https://github.com/miccaiif/top
공식 코드https://github.com/michael-wzhu/PromptCBLUE
공식 코드PromptCBLUE: A Chinese Prompt Tuning Benchmark for the Medical Domain
https://github.com/michellekappl/mt_gender_german
공식 코드https://github.com/michiyasunaga/dragon
공식 코드https://github.com/michiyasunaga/qagnn
공식 코드QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering
https://github.com/microsoft/DEKCOR-CommonsenseQA
공식 코드Fusing Context Into Knowledge Graph for Commonsense Question Answering
https://github.com/microsoft/DeepSpeed
공식 코드https://github.com/microsoft/FLAML
공식 코드Cost-Effective Hyperparameter Optimization for Large Language Model Generation Inference
https://github.com/microsoft/LMOps/tree/main/LLM4Science
공식 코드https://github.com/microsoft/LMOps/tree/main/llma
공식 코드Inference with Reference: Lossless Acceleration of Large Language Models
https://github.com/microsoft/MInference
공식 코드MInference 1.0: Accelerating Pre-filling for Long-Context LLMs via Dynamic Sparse Attention
https://github.com/microsoft/Multilingual-Culture-First-Misgendering-Guardrails
공식 코드A Multilingual, Culture-First Approach to Addressing Misgendering in LLM Applications
https://github.com/microsoft/agdebugger
공식 코드Interactive Debugging and Steering of Multi-Agent AI Systems
https://github.com/microsoft/alympics
공식 코드ALYMPICS: LLM Agents Meet Game Theory -- Exploring Strategic Decision-Making with AI Agents
https://github.com/microsoft/competeai
공식 코드CompeteAI: Understanding the Competition Dynamics in Large Language Model-based Agents
https://github.com/microsoft/conli_hallucination
공식 코드Chain of Natural Language Inference for Reducing Large Language Model Ungrounded Hallucinations
https://github.com/microsoft/defenderbench
공식 코드DefenderBench: A Toolkit for Evaluating Language Agents in Cybersecurity Environments
https://github.com/microsoft/geo-vision-labeler
공식 코드GeoVision Labeler: Zero-Shot Geospatial Classification with Vision and Language Models
https://github.com/microsoft/glinthawk
공식 코드Glinthawk: A Two-Tiered Architecture for Offline LLM Inference
https://github.com/microsoft/llm-rubric
공식 코드https://github.com/microsoft/lmops
공식 코드https://github.com/microsoft/muzic
공식 코드MusicAgent: An AI Agent for Music Understanding and Generation with Large Language Models
https://github.com/microsoft/only_train_once
공식 코드HESSO: Towards Automatic Efficient and User Friendly Any Neural Network Training and Pruning
https://github.com/microsoft/pike-rag
공식 코드PIKE-RAG: sPecIalized KnowledgE and Rationale Augmented Generation
https://github.com/microsoft/rebel
공식 코드https://github.com/microsoft/recai
공식 코드RecAI: Leveraging Large Language Models for Next-Generation Recommender Systems
https://github.com/microsoft/sammo
공식 코드https://github.com/microsoft/simxns
공식 코드https://github.com/microsoft/speecht5
공식 코드WavLLM: Towards Robust and Adaptive Speech Large Language Model
https://github.com/microsoft/synqo
공식 코드MAGIC: Generating Self-Correction Guideline for In-Context Text-to-SQL
https://github.com/microsoft/unilm
공식 코드Language Is Not All You Need: Aligning Perception with Language Models
https://github.com/microsoft/unilm/tree/master/kosmos-2
공식 코드Kosmos-2: Grounding Multimodal Large Language Models to the World
https://github.com/microsoft/wina
공식 코드WINA: Weight Informed Neuron Activation for Accelerating Large Language Model Inference
https://github.com/midiyazhu/micl
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.