https://github.com/aorwall/moatless-tree-search
공식 코드SWE-Search: Enhancing Software Agents with Monte Carlo Tree Search and Iterative Refinement
Papers with Code 아카이브
Knowledge Graph Completion
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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/apartresearch/seqcont_circuits
공식 코드Towards Interpretable Sequence Continuation: Analyzing Shared Circuits in Large Language Models
https://github.com/apoorvumang/CronKGQA
공식 코드https://github.com/apoorvumang/kgt5
공식 코드Sequence-to-Sequence Knowledge Graph Completion and Question Answering
https://github.com/appl-team/appl
공식 코드https://github.com/apple/ml-ferret
공식 코드Ferret: Refer and Ground Anything Anywhere at Any Granularity
https://github.com/apple/ml-hypercloning
공식 코드Scaling Smart: Accelerating Large Language Model Pre-training with Small Model Initialization
https://github.com/apple/ml-interactive-data-augmentation
공식 코드https://github.com/apple/ml-kge
공식 코드Increasing Coverage and Precision of Textual Information in Multilingual Knowledge Graphs
https://github.com/apple/ml-slowfast-llava
공식 코드SlowFast-LLaVA: A Strong Training-Free Baseline for Video Large Language Models
https://github.com/apple/ml-superposition-prompting
공식 코드Superposition Prompting: Improving and Accelerating Retrieval-Augmented Generation
https://github.com/appletea233/llava-st
공식 코드LLaVA-ST: A Multimodal Large Language Model for Fine-Grained Spatial-Temporal Understanding
https://github.com/applied-ai-research-lab/orchestrator-agent-trust
공식 코드https://github.com/applied-machine-learning-lab/g3
공식 코드https://github.com/applied-machine-learning-lab/llmemb
공식 코드LLMEmb: Large Language Model Can Be a Good Embedding Generator for Sequential Recommendation
https://github.com/applied-machine-learning-lab/mesh-tkgr
공식 코드https://github.com/applied-machine-learning-lab/mill
공식 코드MILL: Mutual Verification with Large Language Models for Zero-Shot Query Expansion
https://github.com/april-tools/gekcs
공식 코드How to Turn Your Knowledge Graph Embeddings into Generative Models
https://github.com/araag2/SemEval2024-Task2
공식 코드https://github.com/archersama/uni-ctr
공식 코드A Unified Framework for Multi-Domain CTR Prediction via Large Language Models
https://github.com/archiki/utgendebug
공식 코드https://github.com/arghadippurdue/GraNNite
공식 코드https://github.com/arm-diaz/bio-germline-diseases-bert-ner
공식 코드https://github.com/arminarj/empathic-grounding
공식 코드https://github.com/armitakhn/gilbert
공식 코드https://github.com/artessay/sama
공식 코드TC-RAG:Turing-Complete RAG's Case study on Medical LLM Systems
https://github.com/arthur422tp/hierachical
공식 코드https://github.com/arthurbra/citation-recommendation-kg
공식 코드Citation Recommendation for Research Papers via Knowledge Graphs
https://github.com/arthurleom/dress-llm
공식 코드DRESSing Up LLM: Efficient Stylized Question-Answering via Style Subspace Editing
https://github.com/artificial-scientist-lab/impact4cast
공식 코드Forecasting high-impact research topics via machine learning on evolving knowledge graphs
https://github.com/artificial-scientist-lab/scimuse
공식 코드https://github.com/arturzolkowski/steganographic-evals
공식 코드https://github.com/arvid-pku/atoke
공식 코드History Matters: Temporal Knowledge Editing in Large Language Model
https://github.com/aryan-jadon/synthetic-data-generation-and-evaluation-using-reasoning-models
공식 코드https://github.com/aryopg/biokge
공식 코드https://github.com/asadifar/HCqa
공식 코드HCqa: Hybrid and Complex Question Answering on Textual Corpus and Knowledge Graph
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.