https://github.com/gvclab/vau-r1
공식 코드VAU-R1: Advancing Video Anomaly Understanding via Reinforcement Fine-Tuning
Papers with Code 아카이브
Question Answering
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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/gxym/stgt
공식 코드Video-Language Alignment via Spatio-Temporal Graph Transformer
https://github.com/gy915/dr3
공식 코드https://github.com/gychant/CSKMTermDefn
공식 코드https://github.com/h-j-han/automatic_explicitation
공식 코드Bridging Background Knowledge Gaps in Translation with Automatic Explicitation
https://github.com/h19920918/emr
공식 코드Episodic Memory Reader: Learning What to Remember for Question Answering from Streaming Data
https://github.com/h2oai/h2o-llmstudio
공식 코드https://github.com/hackerchenzhuo/LaKo
공식 코드LaKo: Knowledge-driven Visual Question Answering via Late Knowledge-to-Text Injection
https://github.com/hahahenha/poi-qa
공식 코드A Dataset for Spatiotemporal-Sensitive POI Question Answering
https://github.com/haileyfamo/insqabench
공식 코드InsQABench: Benchmarking Chinese Insurance Domain Question Answering with Large Language Models
https://github.com/haitian-sun/conditionalqa
공식 코드ConditionalQA: A Complex Reading Comprehension Dataset with Conditional Answers
https://github.com/halfrot/ALaRM
공식 코드ALaRM: Align Language Models via Hierarchical Rewards Modeling
https://github.com/halueval-wild/halueval-wild
공식 코드HaluEval-Wild: Evaluating Hallucinations of Language Models in the Wild
https://github.com/hamedhematian/pcoqa
공식 코드https://github.com/hamedhematian/syncqg
공식 코드Consistency Training by Synthetic Question Generation for Conversational Question Answering
https://github.com/hamzakeurti/homomorphismvae
공식 코드Homomorphism Autoencoder -- Learning Group Structured Representations from Observed Transitions
https://github.com/hamzeiehsan/questions-to-geosparql
공식 코드https://github.com/hanane-djeddal/attributed-ir
공식 코드An Evaluation Framework for Attributed Information Retrieval using Large Language Models
https://github.com/hanjiechen/challengeclinicalqa
공식 코드Benchmarking Large Language Models on Answering and Explaining Challenging Medical Questions
https://github.com/hannandarryl/ManyModalQA
공식 코드ManyModalQA: Modality Disambiguation and QA over Diverse Inputs
https://github.com/hannight/adacad
공식 코드AdaCAD: Adaptively Decoding to Balance Conflicts between Contextual and Parametric Knowledge
https://github.com/hansizeng/uia
공식 코드A Personalized Dense Retrieval Framework for Unified Information Access
https://github.com/hanxiao/daanet
공식 코드https://github.com/hanzhou032/xqa-dst
공식 코드XQA-DST: Multi-Domain and Multi-Lingual Dialogue State Tracking
https://github.com/hao-cheng/ds_doc_qa
공식 코드https://github.com/hao840/adem-vl
공식 코드ADEM-VL: Adaptive and Embedded Fusion for Efficient Vision-Language Tuning
https://github.com/haodongze/self-ksel-qans
공식 코드Self-Bootstrapped Visual-Language Model for Knowledge Selection and Question Answering
https://github.com/haonan-li/lamb
공식 코드Location Aware Modular Biencoder for Tourism Question Answering
https://github.com/haoningwu3639/SpatialScore
공식 코드SpatialScore: Towards Unified Evaluation for Multimodal Spatial Understanding
https://github.com/haotianxia/SPORTU
공식 코드SPORTU: A Comprehensive Sports Understanding Benchmark for Multimodal Large Language Models
https://github.com/haozhe-an/sodapop
공식 코드SODAPOP: Open-Ended Discovery of Social Biases in Social Commonsense Reasoning Models
https://github.com/happen2me/cross-gnn
공식 코드GraphextQA: A Benchmark for Evaluating Graph-Enhanced Large Language Models
https://github.com/happen2me/subgraph-retrieval-toolkit
공식 코드https://github.com/happywwy/elabor
공식 코드Elaboration-Generating Commonsense Question Answering at Scale
https://github.com/harryhsing/echoink
공식 코드EchoInk-R1: Exploring Audio-Visual Reasoning in Multimodal LLMs via Reinforcement Learning
https://github.com/harryjeon24/student_performance
공식 코드Modeling Student Performance in Game-Based Learning Environments
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.