https://github.com/leililab/rare
공식 코드RARE: Retrieval-Aware Robustness Evaluation for Retrieval-Augmented Generation Systems
Papers with Code 아카이브
Knowledge Graph Completion
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 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: $(h, r, ?)$.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/leirunlin/text-level-graph-attack
공식 코드Intruding with Words: Towards Understanding Graph Injection Attacks at the Text Level
https://github.com/lemingshen/GPIoT
공식 코드GPIoT: Tailoring Small Language Models for IoT Program Synthesis and Development
https://github.com/lenazellinger/counterfactual_kgr
공식 코드https://github.com/leon1207/video-rag-master
공식 코드Video-RAG: Visually-aligned Retrieval-Augmented Long Video Comprehension
https://github.com/leslie071564/big5-llm-negotiator
공식 코드How Personality Traits Influence Negotiation Outcomes? A Simulation based on Large Language Models
https://github.com/lezhang7/moqagpt
공식 코드MoqaGPT : Zero-Shot Multi-modal Open-domain Question Answering with Large Language Model
https://github.com/lezhang7/rearank
공식 코드REARANK: Reasoning Re-ranking Agent via Reinforcement Learning
https://github.com/lfsszd/cs-drafting
공식 코드https://github.com/lgalke/easy2deeplearn
공식 코드https://github.com/li-hyn/llm_catastrophicforgetting
공식 코드Revisiting Catastrophic Forgetting in Large Language Model Tuning
https://github.com/li-z-q/structrag
공식 코드https://github.com/li001029/Medical_LLM_ASR
공식 코드Searching for Best Practices in Medical Transcription with Large Language Model
https://github.com/lianjiatech/belle
공식 코드https://github.com/liano3/RAG-fairness
공식 코드The Other Side of the Coin: Exploring Fairness in Retrieval-Augmented Generation
https://github.com/lianshumin576/mloandmcts
공식 코드Intelligent Channel Allocation for IEEE 802.11be Multi-Link Operation: When MAB Meets LLM
https://github.com/lightblue-tech/aloftrag
공식 코드ALoFTRAG: Automatic Local Fine Tuning for Retrieval Augmented Generation
https://github.com/lihaoxin2020/summarization-based-document-ids-for-generative-retrieval
공식 코드Summarization-Based Document IDs for Generative Retrieval with Language Models
https://github.com/lijiabei-7/leccr
공식 코드https://github.com/lijiazheng99/aera
공식 코드Distilling ChatGPT for Explainable Automated Student Answer Assessment
https://github.com/lijlansg/trafficgpt
공식 코드TrafficGPT: Viewing, Processing and Interacting with Traffic Foundation Models
https://github.com/likaiucas/obm
공식 코드Extracting polygonal footprints in off-nadir images with Segment Anything Model
https://github.com/limanwang/balancing-rigor-and-utility
공식 코드https://github.com/lin000001/Legilimens
공식 코드Legilimens: Practical and Unified Content Moderation for Large Language Model Services
https://github.com/linchen-65/restruct
공식 코드Large Language Model-driven Meta-structure Discovery in Heterogeneous Information Network
https://github.com/lindazeng979/llm-cmsa
공식 코드Leveraging Large Language Models for Code-Mixed Data Augmentation in Sentiment Analysis
https://github.com/ling-cai/time2box
공식 코드Time in a Box: Advancing Knowledge Graph Completion with Temporal Scopes
https://github.com/linkseed18612254945/finerob
공식 코드Fine-Grained Behavior Simulation with Role-Playing Large Language Model on Social Media
https://github.com/linxyhaha/atspeed
공식 코드Efficient Inference for Large Language Model-based Generative Recommendation
https://github.com/lion-zs/er
공식 코드https://github.com/lion-zs/gie
공식 코드https://github.com/liqs-v2/ctim-rover
공식 코드https://github.com/liqunma/fbi-llm
공식 코드FBI-LLM: Scaling Up Fully Binarized LLMs from Scratch via Autoregressive Distillation
https://github.com/lirt1231/kermit
공식 코드KERMIT: Knowledge Graph Completion of Enhanced Relation Modeling with Inverse Transformation
https://github.com/liseda-lab/seek
공식 코드Explainable Representations for Relation Prediction in Knowledge Graphs
https://github.com/liseda-lab/truewalks
공식 코드Biomedical Knowledge Graph Embeddings with Negative Statements
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.