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Knowledge Graph Completion

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하위 태스크: 4
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: $(h, r, ?)$.

Source: One-Shot Relational Learning for Knowledge Graphs

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/michael-wzhu/PromptCBLUE

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PromptCBLUE: A Chinese Prompt Tuning Benchmark for the Medical Domain

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Evaluating Gender Bias in German Machine Translation

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DeepSpeed Data Efficiency: Improving Deep Learning Model Quality and Training Efficiency via Efficient Data Sampling and Routing

https://github.com/microsoft/FLAML

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Cost-Effective Hyperparameter Optimization for Large Language Model Generation Inference

https://github.com/microsoft/LMOps/tree/main/LLM4Science

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Large Language Model for Science: A Study on P vs. NP

https://github.com/microsoft/LMOps/tree/main/llma

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Inference with Reference: Lossless Acceleration of Large Language Models

https://github.com/microsoft/MInference

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MInference 1.0: Accelerating Pre-filling for Long-Context LLMs via Dynamic Sparse Attention

https://github.com/microsoft/Multilingual-Culture-First-Misgendering-Guardrails

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A Multilingual, Culture-First Approach to Addressing Misgendering in LLM Applications

https://github.com/microsoft/agdebugger

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Interactive Debugging and Steering of Multi-Agent AI Systems

https://github.com/microsoft/alympics

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ALYMPICS: LLM Agents Meet Game Theory -- Exploring Strategic Decision-Making with AI Agents

https://github.com/microsoft/competeai

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CompeteAI: Understanding the Competition Dynamics in Large Language Model-based Agents

https://github.com/microsoft/conli_hallucination

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Chain of Natural Language Inference for Reducing Large Language Model Ungrounded Hallucinations

https://github.com/microsoft/defenderbench

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DefenderBench: A Toolkit for Evaluating Language Agents in Cybersecurity Environments

https://github.com/microsoft/geo-vision-labeler

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GeoVision Labeler: Zero-Shot Geospatial Classification with Vision and Language Models

https://github.com/microsoft/glinthawk

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Glinthawk: A Two-Tiered Architecture for Offline LLM Inference

https://github.com/microsoft/llm-rubric

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LLM-Rubric: A Multidimensional, Calibrated Approach to Automated Evaluation of Natural Language Texts

https://github.com/microsoft/lmops

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On-Policy RL with Optimal Reward Baseline

https://github.com/microsoft/muzic

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MusicAgent: An AI Agent for Music Understanding and Generation with Large Language Models

https://github.com/microsoft/only_train_once

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HESSO: Towards Automatic Efficient and User Friendly Any Neural Network Training and Pruning

https://github.com/microsoft/pike-rag

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PIKE-RAG: sPecIalized KnowledgE and Rationale Augmented Generation

https://github.com/microsoft/rebel

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Relevance Isn't All You Need: Scaling RAG Systems With Inference-Time Compute Via Multi-Criteria Reranking

https://github.com/microsoft/recai

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RecAI: Leveraging Large Language Models for Next-Generation Recommender Systems

https://github.com/microsoft/sammo

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Symbolic Prompt Program Search: A Structure-Aware Approach to Efficient Compile-Time Prompt Optimization

https://github.com/microsoft/simxns

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Allies: Prompting Large Language Model with Beam Search

https://github.com/microsoft/speecht5

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WavLLM: Towards Robust and Adaptive Speech Large Language Model

https://github.com/microsoft/synqo

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MAGIC: Generating Self-Correction Guideline for In-Context Text-to-SQL

https://github.com/microsoft/unilm

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Language Is Not All You Need: Aligning Perception with Language Models

https://github.com/microsoft/unilm/tree/master/kosmos-2

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Kosmos-2: Grounding Multimodal Large Language Models to the World

https://github.com/microsoft/wina

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WINA: Weight Informed Neuron Activation for Accelerating Large Language Model Inference

https://github.com/midiyazhu/micl

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Logit Separability-Driven Samples and Multiple Class-Related Words Selection for Advancing In-Context Learning

https://github.com/mikegu721/agentgroupchat-v2

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AgentGroupChat-V2: Divide-and-Conquer Is What LLM-Based Multi-Agent System Need

https://github.com/mikelixiang88/context-matters

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Why does in-context learning fail sometimes? Evaluating in-context learning on open and closed questions

https://github.com/mikelkn/woah-2025

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Think Like a Person Before Responding: A Multi-Faceted Evaluation of Persona-Guided LLMs for Countering Hate

https://github.com/mileswyn/ich_mllms_validation

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Zero-Shot Multi-modal Large Language Model v.s. Supervised Deep Learning: A Comparative Study on CT-Based Intracranial Hemorrhage Subtyping

https://github.com/milkmilk511/lapt

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Design Principle Transfer in Neural Architecture Search via Large Language Models

https://github.com/milvlg/prophet

공식 코드

Prophet: Prompting Large Language Models with Complementary Answer Heuristics for Knowledge-based Visual Question Answering

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