https://github.com/opengvlab/multi-modality-arena
공식 코드LVLM-eHub: A Comprehensive Evaluation Benchmark for Large Vision-Language Models
Papers with Code 아카이브
Question Answering
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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Question answering can be segmented into domain-specific tasks like community question answering and knowledge-base question answering. Popular benchmark datasets for evaluation question answering systems include SQuAD, HotPotQA, bAbI, TriviaQA, WikiQA, and many others. Models for question answering are typically evaluated on metrics like EM and F1. Some recent top performing models are T5 and XLNet.
( Image credit: SQuAD )
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/opengvlab/multitask-model-selector
공식 코드Foundation Model is Efficient Multimodal Multitask Model Selector
https://github.com/opengvlab/visionllm
공식 코드https://github.com/openlamm/lamm
공식 코드https://github.com/openlifescience-ai/clift
공식 코드CLIFT: Analysing Natural Distribution Shift on Question Answering Models in Clinical Domain
https://github.com/openmatch/activerag
공식 코드ActiveRAG: Autonomously Knowledge Assimilation and Accommodation through Retrieval-Augmented Agents
https://github.com/openmatch/univl-dr
공식 코드https://github.com/openmicg/mcg
공식 코드https://github.com/openmoss/say-i-dont-know
공식 코드https://github.com/openrobotlab/embodiedscan
공식 코드MMScan: A Multi-Modal 3D Scene Dataset with Hierarchical Grounded Language Annotations
https://github.com/openspg/kag
공식 코드KAG: Boosting LLMs in Professional Domains via Knowledge Augmented Generation
https://github.com/opscidia/science-checker
공식 코드Science Checker: Extractive-Boolean Question Answering For Scientific Fact Checking
https://github.com/orange-opensource/coqar
공식 코드https://github.com/orange0629/llm-personas
공식 코드https://github.com/orionw/disinformation-defense
공식 코드Defending Against Disinformation Attacks in Open-Domain Question Answering
https://github.com/oriram/spider
공식 코드https://github.com/oriram/splinter
공식 코드https://github.com/oriyor/reasoning-on-cots
공식 코드Answering Questions by Meta-Reasoning over Multiple Chains of Thought
https://github.com/oriyor/ret-robust
공식 코드Making Retrieval-Augmented Language Models Robust to Irrelevant Context
https://github.com/osome-iu/Rematch-RARE
공식 코드https://github.com/osu-nlp-group/attrscore
공식 코드Automatic Evaluation of Attribution by Large Language Models
https://github.com/osu-nlp-group/hipporag
공식 코드HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models
https://github.com/osu-nlp-group/mqa
공식 코드Multimodal Question Answering for Unified Information Extraction
https://github.com/osu-nlp-group/qa4re
공식 코드Aligning Instruction Tasks Unlocks Large Language Models as Zero-Shot Relation Extractors
https://github.com/osu-nlp-group/seeact
공식 코드https://github.com/osu-slatelab/mapqa
공식 코드https://github.com/overloadedhenry/clinkd
공식 코드ClinKD: Cross-Modal Clinical Knowledge Distiller For Multi-Task Medical Images
https://github.com/ovguyo/captions-in-vqa
공식 코드Enhancing Visual Question Answering through Question-Driven Image Captions as Prompts
https://github.com/ovshake/cobra
공식 코드https://github.com/padas-lab-de/PersonaRAG
공식 코드PersonaRAG: Enhancing Retrieval-Augmented Generation Systems with User-Centric Agents
https://github.com/paddlepaddle/rocketqa
공식 코드https://github.com/pairlab/slotformer
공식 코드SlotFormer: Unsupervised Visual Dynamics Simulation with Object-Centric Models
https://github.com/paitesanshi/characterbox
공식 코드CharacterBox: Evaluating the Role-Playing Capabilities of LLMs in Text-Based Virtual Worlds
https://github.com/panasonicconnect/videomultiagents
공식 코드VideoMultiAgents: A Multi-Agent Framework for Video Question Answering
https://github.com/pangjh3/anllm
공식 코드https://github.com/panushri25/emrQA
공식 코드emrQA: A Large Corpus for Question Answering on Electronic Medical Records
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.