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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/modeltc/llmc

공식 코드

LLMC: Benchmarking Large Language Model Quantization with a Versatile Compression Toolkit

https://github.com/mohanraj-nlp/chatgpt-malaysian-english

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How well ChatGPT understand Malaysian English? An Evaluation on Named Entity Recognition and Relation Extraction

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TimeSeriesGym: A Scalable Benchmark for (Time Series) Machine Learning Engineering Agents

https://github.com/monarch-initiative/dragon-ai-results

공식 코드

Dynamic Retrieval Augmented Generation of Ontologies using Artificial Intelligence (DRAGON-AI)

https://github.com/monarch-initiative/gpt-ontology-completion-analysis

공식 코드

Dynamic Retrieval Augmented Generation of Ontologies using Artificial Intelligence (DRAGON-AI)

https://github.com/moonshotai/kimina-prover-preview

공식 코드

Kimina-Prover Preview: Towards Large Formal Reasoning Models with Reinforcement Learning

https://github.com/moreno98/gradbias

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GradBias: Unveiling Word Influence on Bias in Text-to-Image Generative Models

https://github.com/morganf33/gnr

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Generative News Recommendation

https://github.com/mosesananta/qless

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QLESS: A Quantized Approach for Data Valuation and Selection in Large Language Model Fine-Tuning

https://github.com/mozerwang/loong

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Leave No Document Behind: Benchmarking Long-Context LLMs with Extended Multi-Doc QA

https://github.com/mpi-lab/bc-hoi

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Bilateral Collaboration with Large Vision-Language Models for Open Vocabulary Human-Object Interaction Detection

https://github.com/mramg-bench/mramg

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MRAMG-Bench: A Comprehensive Benchmark for Advancing Multimodal Retrieval-Augmented Multimodal Generation

https://github.com/mrgiovanni/radgpt

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RadGPT: Constructing 3D Image-Text Tumor Datasets

https://github.com/mrzilinxiao/autover

공식 코드

Grounding Language Models for Visual Entity Recognition

https://github.com/mtkresearch/rad-bench

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RAD-Bench: Evaluating Large Language Models Capabilities in Retrieval Augmented Dialogues

https://github.com/muellerlab/synthesizing_interpretable_control_policies

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Synthesizing Interpretable Control Policies through Large Language Model Guided Search

https://github.com/muktac5/ciir-liverag

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CIIR@LiveRAG 2025: Optimizing Multi-Agent Retrieval Augmented Generation through Self-Training

https://github.com/multimodal-art-projection/map-neo

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MAP-Neo: Highly Capable and Transparent Bilingual Large Language Model Series

https://github.com/mungomeng/survival-xsurv

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Merging-Diverging Hybrid Transformer Networks for Survival Prediction in Head and Neck Cancer

https://github.com/murongyue/llm_mot_cascade

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Large Language Model Cascades with Mixture of Thoughts Representations for Cost-efficient Reasoning

https://github.com/mushfiqur11/simpledc-dataset

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Health Text Simplification: An Annotated Corpus for Digestive Cancer Education and Novel Strategies for Reinforcement Learning

https://github.com/mustafakarabag/llmchameleon

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Do LLMs Strategically Reveal, Conceal, and Infer Information? A Theoretical and Empirical Analysis in The Chameleon Game

https://github.com/mutiann/ccc

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On the Role of Conceptualization in Commonsense Knowledge Graph Construction

https://github.com/mxpoliakov/multi-meta-rag

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Multi-Meta-RAG: Improving RAG for Multi-Hop Queries using Database Filtering with LLM-Extracted Metadata

https://github.com/my-ai-university/finite-element-method

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AI-University: An LLM-based platform for instructional alignment to scientific classrooms

https://github.com/myazann/ap-bots

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Improving RAG for Personalization with Author Features and Contrastive Examples

https://github.com/myeon9h/planrag

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PlanRAG: A Plan-then-Retrieval Augmented Generation for Generative Large Language Models as Decision Makers

https://github.com/mysong7nlper/chatgpt_as_keyphrase_extractor

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Large Language Models as Zero-Shot Keyphrase Extractors: A Preliminary Empirical Study

https://github.com/n4feng/ResponseQualityAssessment

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Response Quality Assessment for Retrieval-Augmented Generation via Conditional Conformal Factuality

https://github.com/nadavlab/CPLLM

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CPLLM: Clinical Prediction with Large Language Models

https://github.com/naist-nlp/CC_RAG

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Understanding the Impact of Confidence in Retrieval Augmented Generation: A Case Study in the Medical Domain

https://github.com/namin/llm-verified-with-monte-carlo-tree-search

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VerMCTS: Synthesizing Multi-Step Programs using a Verifier, a Large Language Model, and Tree Search

https://github.com/nan-huo/micro-act

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Micro-Act: Mitigate Knowledge Conflict in Question Answering via Actionable Self-Reasoning

https://github.com/narabzad/genir-evaluation

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A Comparison of Methods for Evaluating Generative IR

https://github.com/narabzad/prompt-sensitivity-relevance-judgements

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A Human-AI Comparative Analysis of Prompt Sensitivity in LLM-Based Relevance Judgment

https://github.com/national-center-for-ai-saudi-arabia/lm-evaluation-harness

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When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model Leaderboards

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.