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Science Question Answering
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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- 보존된 평가 기록
- 10
Image credit: Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ai-systems/unification_reconstruction_explanations
공식 코드Unification-based Reconstruction of Multi-hop Explanations for Science Questions
https://github.com/amazon-science/mm-cot
공식 코드https://github.com/chengtan9907/mc-cot
공식 코드https://github.com/declare-lab/team
공식 코드https://github.com/kakaobrain/honeybee
공식 코드https://github.com/lupantech/ScienceQA
공식 코드Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering
https://github.com/pku-yuangroup/chat-univi
공식 코드https://github.com/t-sciq/t-sciq
공식 코드https://github.com/wwxu21/AMR-SG
공식 코드https://github.com/wwxu21/cgr
공식 코드Exploiting Reasoning Chains for Multi-hop Science Question Answering
https://github.com/hcplab-sysu/causal-vlreasoning
https://github.com/hcplab-sysu/causalvlr
https://github.com/jy0205/lavit
Video-LaVIT: Unified Video-Language Pre-training with Decoupled Visual-Motional Tokenization
https://github.com/luogen1996/lavin
Cheap and Quick: Efficient Vision-Language Instruction Tuning for Large Language Models
https://github.com/masterai-eam/sciqag
https://github.com/pku-yuangroup/video-bench
https://github.com/skyworkai/moe-plus-plus
https://github.com/skyworkai/moh
https://github.com/xqx12/daily-info
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.