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Large Language Model
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/frederikkemarin/bend
HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolution
https://github.com/fredxlpy/cross_lingual_prompt_tuning
https://github.com/fudandisc/reform-eval
MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models
https://github.com/fumiyo0607/KGAN
https://github.com/funaudiollm/cv3-eval
CosyVoice 3: Towards In-the-wild Speech Generation via Scaling-up and Post-training
https://github.com/gair-nlp/abel
Solving Quantitative Reasoning Problems with Language Models
https://github.com/gengchenmai/se-kge
Contextual Graph Attention for Answering Logical Queries over Incomplete Knowledge Graphs
https://github.com/giacoballoccu/rep-path-reasoning-recsys
Reinforcement Recommendation Reasoning through Knowledge Graphs for Explanation Path Quality
https://github.com/giannispana/ails-ntua-at-semeval-2024-task-9-brainteaser
BRAINTEASER: Lateral Thinking Puzzles for Large Language Models
https://github.com/giantonia/KGATv2
https://github.com/gicheonkang/gst-visdial
https://github.com/giuseppefutia/semi
https://github.com/goodevening13/aquakv
Pushing the Limits of Large Language Model Quantization via the Linearity Theorem
https://github.com/goodmike31/pl-asr-bigos-tools
https://github.com/google-deepmind/alphadev
https://github.com/graphcore/distributed-kge-poplar
OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs
https://github.com/graphcore/ogb-lsc-pcqm4mv2
OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs
https://github.com/greenbitai/low_bit_llama
OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models
https://github.com/guillaumejaume/tuto-dl-on-graphs
https://github.com/guixiangyu1/KGE-HAKE-master
Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction
https://github.com/gusye1234/nano-graphrag
From Local to Global: A Graph RAG Approach to Query-Focused Summarization
https://github.com/gzhu06/cacophony
https://github.com/hahnyuan/llmviewer
https://github.com/haiquanlu/alphapruning
Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias
https://github.com/hanguo97/flute
Pushing the Limits of Large Language Model Quantization via the Linearity Theorem
https://github.com/hannaabiakl/psychic
PSYCHIC: A Neuro-Symbolic Framework for Knowledge Graph Question-Answering Grounding
https://github.com/hazyresearch/flash-attention
Efficient Memory Management for Large Language Model Serving with PagedAttention
https://github.com/hazyresearch/legalbench
Learning to Generate Instruction Tuning Datasets for Zero-Shot Task Adaptation
https://github.com/heegyukim/open-korean-instructions
Prometheus: Inducing Fine-grained Evaluation Capability in Language Models
https://github.com/hemingkx/Spec-Bench
https://github.com/heyuan9/medscore
https://github.com/hieuphan33/cvpr2024_mavl
https://github.com/hitz-zentroa/eval-MCG-COLING-2025
A LLM-Based Ranking Method for the Evaluation of Automatic Counter-Narrative Generation
https://github.com/hitz-zentroa/lm-contamination
NLP Evaluation in trouble: On the Need to Measure LLM Data Contamination for each Benchmark
https://github.com/hitz-zentroa/whisper-lm-transformers
Whisper-LM: Improving ASR Models with Language Models for Low-Resource Languages
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.