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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/adlnlp/msg-chart
공식 코드https://github.com/adnenabdessaied/NSVD
공식 코드https://github.com/adrianbzg/tinyroberta-distillation-qa-es
공식 코드Language Model Knowledge Distillation for Efficient Question Answering in Spanish
https://github.com/ag-gipp/MathQa
공식 코드Introducing MathQA -- A Math-Aware Question Answering System
https://github.com/ag-gipp/formula-concept-retrieval
공식 코드Discovery and Recognition of Formula Concepts using Machine Learning
https://github.com/ag-sc/neodudes
공식 코드https://github.com/ag1988/injecting_numeracy
공식 코드https://github.com/ag2ai/simpledoc
공식 코드SimpleDoc: Multi-Modal Document Understanding with Dual-Cue Page Retrieval and Iterative Refinement
https://github.com/agarwalishika/delift
공식 코드DELIFT: Data Efficient Language model Instruction Fine Tuning
https://github.com/agent-husky/husky-v1
공식 코드Husky: A Unified, Open-Source Language Agent for Multi-Step Reasoning
https://github.com/agenticir-lab/othink-r1
공식 코드OThink-R1: Intrinsic Fast/Slow Thinking Mode Switching for Over-Reasoning Mitigation
https://github.com/ahalterman/event_location
공식 코드https://github.com/ahalterman/mordecai3
공식 코드https://github.com/ahalterman/ngec
공식 코드Creating Custom Event Data Without Dictionaries: A Bag-of-Tricks
https://github.com/ahjeongseo/MASN-pytorch
공식 코드Attend What You Need: Motion-Appearance Synergistic Networks for Video Question Answering
https://github.com/ahmad-shirazi/AnnotMLLM
공식 코드https://github.com/ahmedehabb/From-Roots-to-Rewards-Dynamic-Tree-Reasoning-with-RL
공식 코드https://github.com/ahmedmagdiosman/clevr-xai
공식 코드Ground Truth Evaluation of Neural Network Explanations with CLEVR-XAI
https://github.com/ahmedmagdiosman/simply-clevr-dataset
공식 코드Ground Truth Evaluation of Neural Network Explanations with CLEVR-XAI
https://github.com/ahydchh/impromptu-vla
공식 코드Impromptu VLA: Open Weights and Open Data for Driving Vision-Language-Action Models
https://github.com/ai-for-science/mozi
공식 코드MoZIP: A Multilingual Benchmark to Evaluate Large Language Models in Intellectual Property
https://github.com/ai-in-health/clinicbench
공식 코드Large Language Models in the Clinic: A Comprehensive Benchmark
https://github.com/ai-se/semi-supervised
공식 코드When Less is More: On the Value of "Co-training" for Semi-Supervised Software Defect Predictors
https://github.com/ai-systems/hybrid_autoregressive_inference
공식 코드Hybrid Autoregressive Inference for Scalable Multi-hop Explanation Regeneration
https://github.com/ai-systems/unification_reconstruction_explanations
공식 코드Unification-based Reconstruction of Multi-hop Explanations for Science Questions
https://github.com/ai4africagroup/telcom_llm
공식 코드QMOS: Enhancing LLMs for Telecommunication with Question Masked loss and Option Shuffling
https://github.com/aibee00/socraticquestioning
공식 코드Socratic Questioning: Learn to Self-guide Multimodal Reasoning in the Wild
https://github.com/aic-factcheck/multilingual-fact-checking
공식 코드Pipeline and Dataset Generation for Automated Fact-checking in Almost Any Language
https://github.com/aida-ugent/llm-ideology-analysis
공식 코드Large Language Models Reflect the Ideology of their Creators
https://github.com/aigents/aigents-java-nlp
공식 코드Natural Language Generation Using Link Grammar for General Conversational Intelligence
https://github.com/aiintelligentsystems/next-level-bert
공식 코드NextLevelBERT: Masked Language Modeling with Higher-Level Representations for Long Documents
https://github.com/ailab-cvc/vl-gpt
공식 코드VL-GPT: A Generative Pre-trained Transformer for Vision and Language Understanding and Generation
https://github.com/aim3-ruc/mpmqa
공식 코드https://github.com/aimagelab/reflectiva
공식 코드Augmenting Multimodal LLMs with Self-Reflective Tokens for Knowledge-based Visual Question Answering
https://github.com/aimbrain/vqa-project
공식 코드Learning Conditioned Graph Structures for Interpretable Visual Question Answering
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.