https://github.com/vahidaz/llamarec-lkg-rag
공식 코드LlamaRec-LKG-RAG: A Single-Pass, Learnable Knowledge Graph-RAG Framework for LLM-Based Ranking
Papers with Code 아카이브
Large Language Model
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/valence-labs/TxPert
공식 코드https://github.com/valentyn1boreiko/llm-threat-model
공식 코드A Realistic Threat Model for Large Language Model Jailbreaks
https://github.com/valuebyte-ai/awesome-llm-psychometrics
공식 코드Large Language Model Psychometrics: A Systematic Review of Evaluation, Validation, and Enhancement
https://github.com/vanderschaarlab/discopop
공식 코드Discovering Preference Optimization Algorithms with and for Large Language Models
https://github.com/vanderschaarlab/l2mac
공식 코드L2MAC: Large Language Model Automatic Computer for Extensive Code Generation
https://github.com/vasilikikou/memegraphs
공식 코드https://github.com/vasiliskatr/faaf
공식 코드FaaF: Facts as a Function for the evaluation of generated text
https://github.com/vbdi/divprune
공식 코드DivPrune: Diversity-based Visual Token Pruning for Large Multimodal Models
https://github.com/vcahlik/reasoning-grounded-explanations
공식 코드Reasoning-Grounded Natural Language Explanations for Language Models
https://github.com/vcskaushik/LLMzip
공식 코드LLMZip: Lossless Text Compression using Large Language Models
https://github.com/vec-ai/wikihow-tiir
공식 코드https://github.com/vectara/FaithJudge
공식 코드Benchmarking LLM Faithfulness in RAG with Evolving Leaderboards
https://github.com/vectara/faithbench
공식 코드FaithBench: A Diverse Hallucination Benchmark for Summarization by Modern LLMs
https://github.com/vectara/mirage-bench
공식 코드MIRAGE-Bench: Automatic Multilingual Benchmark Arena for Retrieval-Augmented Generation Systems
https://github.com/vectorinstitute/fed-rag
공식 코드FedRAG: A Framework for Fine-Tuning Retrieval-Augmented Generation Systems
https://github.com/vectozavr/llm-hessian
공식 코드Hessian of Perplexity for Large Language Models by PyTorch autograd (Open Source)
https://github.com/vectozavr/thanos
공식 코드Thanos: A Block-wise Pruning Algorithm for Efficient Large Language Model Compression
https://github.com/vendi12/semantic_coherence
공식 코드https://github.com/verdurechen/sos-retrieval-loop
공식 코드https://github.com/veronicium/codebenchgen
공식 코드CodeBenchGen: Creating Scalable Execution-based Code Generation Benchmarks
https://github.com/via-research/vtrain
공식 코드https://github.com/vicentvankor/sun-shine
공식 코드https://github.com/vicky-wil/monitor
공식 코드https://github.com/vida-nyu/satori-assistance
공식 코드Satori: Towards Proactive AR Assistant with Belief-Desire-Intention User Modeling
https://github.com/vijaydwivedi75/gnn-lspe
공식 코드Graph Neural Networks with Learnable Structural and Positional Representations
https://github.com/vinairesearch/recgpt
공식 코드RecGPT: Generative Pre-training for Text-based Recommendation
https://github.com/vinairesearch/whoqa
공식 코드Who's Who: Large Language Models Meet Knowledge Conflicts in Practice
https://github.com/vincentkoc/tiny_qa_benchmark_pp
공식 코드https://github.com/vinceouti/citynavagent
공식 코드https://github.com/vinsontang1/slidecoder
공식 코드SlideCoder: Layout-aware RAG-enhanced Hierarchical Slide Generation from Design
https://github.com/vinusankars/reliability-of-ai-text-detectors
공식 코드https://github.com/violet24k/sphere
공식 코드SpherE: Expressive and Interpretable Knowledge Graph Embedding for Set Retrieval
https://github.com/vipulraheja/coedit
공식 코드https://github.com/virallab/turkishbertweet
공식 코드TurkishBERTweet: Fast and Reliable Large Language Model for Social Media Analysis
https://github.com/vishalpallagani/fable
공식 코드FABLE: A Novel Data-Flow Analysis Benchmark on Procedural Text for Large Language Model Evaluation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.