Papers with Code 아카이브
Long-Context Understanding
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 0
- 평가표
- 5
- 보존된 평가 기록
- 39
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/hhhuang/cag
https://github.com/ilyagusev/ping_pong_bench
https://github.com/internlm/internlm
https://github.com/jackaduma/ChatGLM-LoRA-RLHF-PyTorch
https://github.com/kuk/rulm-sbs2
https://github.com/lflage/openfactscore
https://github.com/lilakk/booookscore
FABLES: Evaluating faithfulness and content selection in book-length summarization
https://github.com/lm-sys/routellm
https://github.com/marzenakrp/nocha
FABLES: Evaluating faithfulness and content selection in book-length summarization
https://github.com/modelscope/modelscope
https://github.com/mungg/OneRuler
RULER: What's the Real Context Size of Your Long-Context Language Models?
https://github.com/nvidia/ruler
RULER: What's the Real Context Size of Your Long-Context Language Models?
https://github.com/ojiyumm/longbench_rwkv
LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding
https://github.com/ojiyumm/mt_bench_rwkv
https://github.com/open-compass/opencompass
RULER: What's the Real Context Size of Your Long-Context Language Models?
https://github.com/opengvlab/multi-modality-arena
https://github.com/pwc-1/Paper-9/tree/main/instructblip
InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning
https://github.com/pwc-1/Paper-9/tree/main/internlm
https://github.com/ruixiangcui/agieval
https://github.com/shmsw25/factscore
https://github.com/snorkel-ai/long-context-eval
LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding
https://github.com/sustcsonglin/flash-linear-attention
https://github.com/tabtoyou/kollava
InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning
https://github.com/theoremone/llm-benchmarker-suite
https://github.com/thudm/chatglm
https://github.com/thudm/chatglm2-6b
https://github.com/thudm/chatglm3
https://github.com/torchrwkv/flash-linear-attention
https://github.com/unispac/visual-adversarial-examples-jailbreak-large-language-models
https://github.com/valdecy/pybibx
https://github.com/zach-zhiling-zheng/reticular_chemist
https://github.com/zetangforward/lcm_stack
L-CiteEval: Do Long-Context Models Truly Leverage Context for Responding?
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.