https://github.com/mikewangwzhl/vidil
공식 코드Language Models with Image Descriptors are Strong Few-Shot Video-Language Learners
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/mila-aia/alloprof-data
공식 코드https://github.com/milvlg/bst
공식 코드Bilaterally Slimmable Transformer for Elastic and Efficient Visual Question Answering
https://github.com/milvlg/prophet
공식 코드https://github.com/minbeomkim/advisorqa
공식 코드https://github.com/mineanybuild/mineanybuild
공식 코드MineAnyBuild: Benchmarking Spatial Planning for Open-world AI Agents
https://github.com/mingdachen/syntactic-template-generation
공식 코드Leveraging Natural Supervision for Language Representation Learning and Generation
https://github.com/mingdali6717/ensemble-of-retrievers
공식 코드Unraveling and Mitigating Retriever Inconsistencies in Retrieval-Augmented Large Language Models
https://github.com/mingliangzhang2018/PGDP
공식 코드https://github.com/mingxilii/localegn
공식 코드Few-Sample Traffic Prediction with Graph Networks using Locale as Relational Inductive Biases
https://github.com/minhquan6203/vitextvqa-dataset
공식 코드https://github.com/minhtriet2397/uit-vicov19qa
공식 코드UIT-ViCoV19QA: A Dataset for COVID-19 Community-based Question Answering on Vietnamese Language
https://github.com/minhvuong2000/llmreasoncert
공식 코드Direct Evaluation of Chain-of-Thought in Multi-hop Reasoning with Knowledge Graphs
https://github.com/minnesotanlp/mpo
공식 코드Toward Evaluative Thinking: Meta Policy Optimization with Evolving Reward Models
https://github.com/mintelligence-group/cmfeed
공식 코드Synthesizing Sentiment-Controlled Feedback For Multimodal Text and Image Data
https://github.com/mir-mu/isbiased
공식 코드https://github.com/mirbostani/QA-KD-AL
공식 코드Improving Question Answering Performance Using Knowledge Distillation and Active Learning
https://github.com/mirthai/med3dvlm
공식 코드Med3DVLM: An Efficient Vision-Language Model for 3D Medical Image Analysis
https://github.com/mistralai/mistral-src
공식 코드https://github.com/mit-han-lab/lite-transformer
공식 코드https://github.com/mit-han-lab/llm-awq
공식 코드https://github.com/mit-ll/linkq
공식 코드LinkQ: An LLM-Assisted Visual Interface for Knowledge Graph Question-Answering
https://github.com/miulab/convadr-qa
공식 코드Open-Domain Conversational Question Answering with Historical Answers
https://github.com/mizuumi/JDocQA
공식 코드JDocQA: Japanese Document Question Answering Dataset for Generative Language Models
https://github.com/mjc92/buffer_overrun_memory_networks
공식 코드End-to-End Prediction of Buffer Overruns from Raw Source Code via Neural Memory Networks
https://github.com/mjy1111/bake
공식 코드Untying the Reversal Curse via Bidirectional Language Model Editing
https://github.com/ml-jku/SDLG
공식 코드Semantically Diverse Language Generation for Uncertainty Estimation in Language Models
https://github.com/ml-research/ILLUME
공식 코드ILLUME: Rationalizing Vision-Language Models through Human Interactions
https://github.com/ml-research/slash
공식 코드https://github.com/mlo-lab/bvcd_generative_models
공식 코드A Bias-Variance-Covariance Decomposition of Kernel Scores for Generative Models
https://github.com/mlpc-ucsd/bliva
공식 코드BLIVA: A Simple Multimodal LLM for Better Handling of Text-Rich Visual Questions
https://github.com/mlpen/Nystromformer
공식 코드Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention
https://github.com/mlvlab/Flipped-VQA
공식 코드Large Language Models are Temporal and Causal Reasoners for Video Question Answering
https://github.com/mlvlab/MELTR
공식 코드MELTR: Meta Loss Transformer for Learning to Fine-tune Video Foundation Models
https://github.com/mlvlab/nutrea
공식 코드NuTrea: Neural Tree Search for Context-guided Multi-hop KGQA
https://github.com/mlvlab/ovqa
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.