https://github.com/salesforce/lavis
공식 코드InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning
Papers with Code 아카이브
Long-Context Understanding
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/skyriver-2000/rulearena
공식 코드RuleArena: A Benchmark for Rule-Guided Reasoning with LLMs in Real-World Scenarios
https://github.com/srhm-ca/tldm-benchmark
공식 코드Too Long, Didn't Model: Decomposing LLM Long-Context Understanding With Novels
https://github.com/the-finai/fino1
공식 코드Fino1: On the Transferability of Reasoning Enhanced LLMs to Finance
https://github.com/thu-nics/moa
공식 코드MoA: Mixture of Sparse Attention for Automatic Large Language Model Compression
https://github.com/thudm/cogvlm
공식 코드https://github.com/thudm/glm-130b
공식 코드https://github.com/thudm/longbench
공식 코드LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding
https://github.com/thunlp/llava-uhd
공식 코드LLaVA-UHD: an LMM Perceiving Any Aspect Ratio and High-Resolution Images
https://github.com/tiger-ai-lab/longiclbench
공식 코드https://github.com/ukplab/naacl2025-token-weighting
공식 코드https://github.com/wang-ml-lab/multimodal-needle-in-a-haystack
공식 코드https://github.com/wangyu-ustc/memoryllm
공식 코드https://github.com/wujunjie1998/ref-long
공식 코드Ref-Long: Benchmarking the Long-context Referencing Capability of Long-context Language Models
https://github.com/x-plug/mplug-owl
공식 코드mPLUG-Owl2: Revolutionizing Multi-modal Large Language Model with Modality Collaboration
https://github.com/yarayx/livelongbench
공식 코드LiveLongBench: Tackling Long-Context Understanding for Spoken Texts from Live Streams
https://github.com/yujielu10/seeker
공식 코드From Text to Pixel: Advancing Long-Context Understanding in MLLMs
https://github.com/2023-MindSpore-4/Code12/tree/main/MindFormers/glm
https://github.com/2023-MindSpore-4/Code12/tree/main/MindFormers/glm3
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/cogvlm
https://github.com/AUCOHL/RTL-Repo
https://github.com/InternLM/opencompass
RULER: What's the Real Context Size of Your Long-Context Language Models?
https://github.com/MS-P3/code3/tree/main/instructblip
InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning
https://github.com/MS-P3/code5/tree/main/cogvlm
https://github.com/NVIDIA/Star-Attention
RULER: What's the Real Context Size of Your Long-Context Language Models?
https://github.com/PAIR-code/llm-comparator
https://github.com/THUDM/CogAgent
https://github.com/THUDM/GLM
https://github.com/bjoernpl/fasteval
https://github.com/dlvuldet/primevul
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
https://github.com/dongping-chen/mllm-as-a-judge
https://github.com/emrgnt-cmplxty/zero-shot-replication
https://github.com/eternityyw/tram-benchmark
https://github.com/ethz-privsec/superhuman-ai-consistency
https://github.com/ethz-spylab/superhuman-ai-consistency
https://github.com/formulamonks/llm-benchmarker-suite
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.