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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/randallbalestriero/splinellm

공식 코드

Characterizing Large Language Model Geometry Helps Solve Toxicity Detection and Generation

https://github.com/ranking-agent/edgar

공식 코드

Explainable Enrichment-Driven GrAph Reasoner (EDGAR) for Large Knowledge Graphs with Applications in Drug Repurposing

https://github.com/raphaelmerx/tulun

공식 코드

TULUN: Transparent and Adaptable Low-resource Machine Translation

https://github.com/ratschlab/mmugl

공식 코드

Multi-modal Graph Learning over UMLS Knowledge Graphs

https://github.com/rattlesnakey/cofitune

공식 코드

Balancing Speciality and Versatility: a Coarse to Fine Framework for Supervised Fine-tuning Large Language Model

https://github.com/ravoxsg/efficient_unified_crs

공식 코드

Parameter-Efficient Conversational Recommender System as a Language Processing Task

https://github.com/rayleizhu/vllm-ra

공식 코드

RelayAttention for Efficient Large Language Model Serving with Long System Prompts

https://github.com/raynorlee/kg-entitytyping

공식 코드

The Integration of Semantic and Structural Knowledge in Knowledge Graph Entity Typing

https://github.com/razvandu/conciserl

공식 코드

ConciseRL: Conciseness-Guided Reinforcement Learning for Efficient Reasoning Models

https://github.com/razvandu/layerwisequant

공식 코드

Layer-Wise Quantization: A Pragmatic and Effective Method for Quantizing LLMs Beyond Integer Bit-Levels

https://github.com/razvanip13/towards_lighter_and_robust_evaluation

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Towards Lighter and Robust Evaluation for Retrieval Augmented Generation

https://github.com/rbturnbull/hespi

공식 코드

Hespi: A pipeline for automatically detecting information from hebarium specimen sheets

https://github.com/rcgai/simplyretrieve

공식 코드

SimplyRetrieve: A Private and Lightweight Retrieval-Centric Generative AI Tool

https://github.com/rdnfn/icai

공식 코드

Inverse Constitutional AI: Compressing Preferences into Principles

https://github.com/real-stanford/reflect

공식 코드

REFLECT: Summarizing Robot Experiences for Failure Explanation and Correction

https://github.com/reallmasic/consmax

공식 코드

ConSmax: Hardware-Friendly Alternative Softmax with Learnable Parameters

https://github.com/reason-wang/instructllm

공식 코드

Demystifying Instruction Mixing for Fine-tuning Large Language Models

https://github.com/rebelsnlu-jaist/llmu-robustness

공식 코드

Improving LLM Unlearning Robustness via Random Perturbations

https://github.com/recsys-lab/rag-visualrec

공식 코드

RAG-VisualRec: An Open Resource for Vision- and Text-Enhanced Retrieval-Augmented Generation in Recommendation

https://github.com/redwoodresearch/alignment_faking_public

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Alignment faking in large language models

https://github.com/regionch/sprec

공식 코드

SPRec: Leveraging Self-Play to Debias Preference Alignment for Large Language Model-based Recommendations

https://github.com/reml-group/deliberation-on-priors

공식 코드

Deliberation on Priors: Trustworthy Reasoning of Large Language Models on Knowledge Graphs

https://github.com/reml-group/dog

공식 코드

Debate on Graph: a Flexible and Reliable Reasoning Framework for Large Language Models

https://github.com/ren258/ARENA

공식 코드

Effective and Transparent RAG: Adaptive-Reward Reinforcement Learning for Decision Traceability

https://github.com/renli1024/se-gnn

공식 코드

How Does Knowledge Graph Embedding Extrapolate to Unseen Data: A Semantic Evidence View

https://github.com/renshuhuai-andy/timechat

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TimeChat: A Time-sensitive Multimodal Large Language Model for Long Video Understanding

https://github.com/renzelou/awesome-instruction-learning

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Large Language Model Instruction Following: A Survey of Progresses and Challenges

https://github.com/responsibleailab/s3llm

공식 코드

S3LLM: Large-Scale Scientific Software Understanding with LLMs using Source, Metadata, and Document

https://github.com/rezacsedu/DDI-prediction-KG-embeddings-Conv-LSTM

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Drug-Drug Interaction Prediction Based on Knowledge Graph Embeddings and Convolutional-LSTM Network

https://github.com/rezzfayyazi/lea

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LLM Embedding-based Attribution (LEA): Quantifying Source Contributions to Generative Model's Response for Vulnerability Analysis

https://github.com/rgu-iit-bt/cbr-for-legal-rag

공식 코드

CBR-RAG: Case-Based Reasoning for Retrieval Augmented Generation in LLMs for Legal Question Answering

https://github.com/rhaldarpurdue/kldo

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LLM Safety Alignment is Divergence Estimation in Disguise

https://github.com/ricardo-larosa/tree-of-thought-llm

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Can Github issues be solved with Tree Of Thoughts?

https://github.com/richard-peng-xia/mmed-rag

공식 코드

MMed-RAG: Versatile Multimodal RAG System for Medical Vision Language Models

https://github.com/richard-peng-xia/rule

공식 코드

RULE: Reliable Multimodal RAG for Factuality in Medical Vision Language Models

https://github.com/rickyskywalker/synthesis_step-by-step_official

공식 코드

Let's Synthesize Step by Step: Iterative Dataset Synthesis with Large Language Models by Extrapolating Errors from Small Models

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