https://github.com/LUMII-Syslab/shuffle-exchange
공식 코드Neural Shuffle-Exchange Networks -- Sequence Processing in O(n log n) Time
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/LZ-CH/DSPNet
공식 코드DSPNet: Dual-vision Scene Perception for Robust 3D Question Answering
https://github.com/LabARSS/question-complextiy-estimation
공식 코드When an LLM is apprehensive about its answers -- and when its uncertainty is justified
https://github.com/LeoLee7/Directional_guidance
공식 코드Right this way: Can VLMs Guide Us to See More to Answer Questions?
https://github.com/LiberAI/NSpM
공식 코드Exploring Sequence-to-Sequence Models for SPARQL Pattern Composition
https://github.com/LibertFan/AI_Hospital
공식 코드AI Hospital: Benchmarking Large Language Models in a Multi-agent Medical Interaction Simulator
https://github.com/Libo1023/LEAP
공식 코드Large Language Models as Interpolated and Extrapolated Event Predictors
https://github.com/LinDuoming/LogEval
공식 코드LogEval: A Comprehensive Benchmark Suite for Large Language Models In Log Analysis
https://github.com/Linvyl/DAM-QA
공식 코드Describe Anything Model for Visual Question Answering on Text-rich Images
https://github.com/LiqunChen0606/Graph-Optimal-Transport
공식 코드https://github.com/LuJunru/CHIME
공식 코드CHIME: Cross-passage Hierarchical Memory Network for Generative Review Question Answering
https://github.com/LuciusLan/Visual-RAG
공식 코드https://github.com/Luffy03/UniBiomed
공식 코드UniBiomed: A Universal Foundation Model for Grounded Biomedical Image Interpretation
https://github.com/LukasMut/Subjective_QA
공식 코드https://github.com/LuoweiZhou/VLP
공식 코드Unified Vision-Language Pre-Training for Image Captioning and VQA
https://github.com/MAGIC-AI4Med/ChestX-Reasoner
공식 코드https://github.com/MAGICS-LAB/Chain-of-Actions
공식 코드Chain-of-Action: Faithful and Multimodal Question Answering through Large Language Models
https://github.com/MARIO-Math-Reasoning/Super_MARIO
공식 코드https://github.com/MI2DataLab/nlp_interpretability_framework
공식 코드Are you tough enough? Framework for Robustness Validation of Machine Comprehension Systems
https://github.com/MILVLG/activitynet-qa
공식 코드ActivityNet-QA: A Dataset for Understanding Complex Web Videos via Question Answering
https://github.com/MILVLG/anetqa-code
공식 코드ANetQA: A Large-scale Benchmark for Fine-grained Compositional Reasoning over Untrimmed Videos
https://github.com/MILVLG/mcan-vqa
공식 코드Deep Modular Co-Attention Networks for Visual Question Answering
https://github.com/MILVLG/mmnas
공식 코드https://github.com/MJinXiang/Reasoning-Table
공식 코드Reasoning-Table: Exploring Reinforcement Learning for Table Reasoning
https://github.com/MLforHealth/rl_representations
공식 코드An Empirical Study of Representation Learning for Reinforcement Learning in Healthcare
https://github.com/MM-IR/DualVGR-VideoQA
공식 코드DualVGR: A Dual-Visual Graph Reasoning Unit for Video Question Answering
https://github.com/MUICT-SERU/MMT4NL
공식 코드Test It Before You Trust It: Applying Software Testing for Trustworthy In-context Learning
https://github.com/MaastrichtU-IDS/LMKBC-2023
공식 코드Expanding the Vocabulary of BERT for Knowledge Base Construction
https://github.com/MalihehIzadi/SOPI_stackoverflow_answer_quality
공식 코드Improving Quality of a Post's Set of Answers in Stack Overflow
https://github.com/MalvinaNikandrou/contvqa
공식 코드Task Formulation Matters When Learning Continually: A Case Study in Visual Question Answering
https://github.com/MalvinaNikandrou/crope
공식 코드CROPE: Evaluating In-Context Adaptation of Vision and Language Models to Culture-Specific Concepts
https://github.com/MalvinaNikandrou/mafed
공식 코드Enhancing Continual Learning in Visual Question Answering with Modality-Aware Feature Distillation
https://github.com/Maniyantingliu/generation_with_dynamic_vocabulary
공식 코드https://github.com/MapEval/MapEval-API
공식 코드MapEval: A Map-Based Evaluation of Geo-Spatial Reasoning in Foundation Models
https://github.com/MapEval/MapEval-Textual
공식 코드MapEval: A Map-Based Evaluation of Geo-Spatial Reasoning in Foundation Models
https://github.com/MapEval/MapEval-Visual
공식 코드MapEval: A Map-Based Evaluation of Geo-Spatial Reasoning in Foundation Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.