https://github.com/mlvlab/qat
공식 코드Relation-Aware Language-Graph Transformer for Question Answering
Papers with Code 아카이브
Question Answering
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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/mlvlab/vid-tldr
공식 코드vid-TLDR: Training Free Token merging for Light-weight Video Transformer
https://github.com/mmadmax/culfit
공식 코드CulFiT: A Fine-grained Cultural-aware LLM Training Paradigm via Multilingual Critique Data Synthesis
https://github.com/mmahbub/qa-system-for-injection-drug-use
공식 코드Question-Answering System Extracts Information on Injection Drug Use from Clinical Notes
https://github.com/mmdocrag/mmdocrag
공식 코드Benchmarking Retrieval-Augmented Multimomal Generation for Document Question Answering
https://github.com/mobarakol/pitvqa
공식 코드PitVQA: Image-grounded Text Embedding LLM for Visual Question Answering in Pituitary Surgery
https://github.com/modelscope/ms-swift
공식 코드https://github.com/modelscope/swift
공식 코드https://github.com/modeltc/mqbench
공식 코드QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization
https://github.com/mohamedhisham20/truthfulqa_phi
공식 코드https://github.com/mohammed-elkomy/quran-qa
공식 코드https://github.com/molingbo/inspired
공식 코드Towards Transparent Interactive Semantic Parsing via Step-by-Step Correction
https://github.com/momohuang/FlowQA
공식 코드FlowQA: Grasping Flow in History for Conversational Machine Comprehension
https://github.com/momohuang/FusionNet-NLI
공식 코드FusionNet: Fusing via Fully-Aware Attention with Application to Machine Comprehension
https://github.com/moonshotai/kimi-audio
공식 코드https://github.com/moramisu/qft
공식 코드Design as Desired: Utilizing Visual Question Answering for Multimodal Pre-training
https://github.com/moramisu/qft-miccai2024
공식 코드Design as Desired: Utilizing Visual Question Answering for Multimodal Pre-training
https://github.com/moreno98/gradbias
공식 코드GradBias: Unveiling Word Influence on Bias in Text-to-Image Generative Models
https://github.com/morningmoni/GAR
공식 코드Generation-Augmented Retrieval for Open-domain Question Answering
https://github.com/morty-xu/tcc-bench
공식 코드TCC-Bench: Benchmarking the Traditional Chinese Culture Understanding Capabilities of MLLMs
https://github.com/mosheber/token_elimination
공식 코드Optimizing Retrieval-augmented Reader Models via Token Elimination
https://github.com/moured/refchartqa
공식 코드RefChartQA: Grounding Visual Answer on Chart Images through Instruction Tuning
https://github.com/mozerwang/loong
공식 코드Leave No Document Behind: Benchmarking Long-Context LLMs with Extended Multi-Doc QA
https://github.com/mpsc-umbc/efficient-vision-language-models-a-survey
공식 코드https://github.com/mr-kenlee/cloze
공식 코드https://github.com/mrfanuel/learningcontinuousdpps.jl
공식 코드Nonparametric estimation of continuous DPPs with kernel methods
https://github.com/mrinal18/MAMBA_QA
공식 코드Multilingual State Space Models for Structured Question Answering in Indic Languages
https://github.com/mrjleo/ranking-models
공식 코드Exploiting Sentence-Level Representations for Passage Ranking
https://github.com/mrqa/MRQA-Shared-Task-2019
공식 코드MRQA 2019 Shared Task: Evaluating Generalization in Reading Comprehension
https://github.com/mshahabsepehri/mediconfusion
공식 코드https://github.com/mshukor/ima-lmms
공식 코드https://github.com/msiam/pixfoundation
공식 코드PixFoundation: Are We Heading in the Right Direction with Pixel-level Vision Foundation Models?
https://github.com/msiip/medm-vl
공식 코드https://github.com/msmilevski/nrl
공식 코드Applying recent advances in Visual Question Answering to Record Linkage
https://github.com/mtkresearch/mr-models
공식 코드https://github.com/mu-cai/TemporalBench
공식 코드TemporalBench: Benchmarking Fine-grained Temporal Understanding for Multimodal Video Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.