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Question Answering

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하위 태스크: 19
평가표: 153
보존된 평가 기록: 1,781

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/iiis-ai/iterativequestioncomposing

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Augmenting Math Word Problems via Iterative Question Composing

https://github.com/iitmnlp/BERT-Analysis-RCQA

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Towards Interpreting BERT for Reading Comprehension Based QA

https://github.com/ikb-a/systematicity-gap-in-vqa

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Attribute Diversity Determines the Systematicity Gap in VQA

https://github.com/ilija139/vqa-soft

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A Simple Loss Function for Improving the Convergence and Accuracy of Visual Question Answering Models

https://github.com/illuin-tech/grouse

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GroUSE: A Benchmark to Evaluate Evaluators in Grounded Question Answering

https://github.com/illusionhi/probingprivacy

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Watch Out Your Album! On the Inadvertent Privacy Memorization in Multi-Modal Large Language Models

https://github.com/imagegridworth/IG-VLM

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An Image Grid Can Be Worth a Video: Zero-shot Video Question Answering Using a VLM

https://github.com/imatge-upc/vqa-2016-cvprw

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Open-Ended Visual Question-Answering

https://github.com/iml-dkfz/sure-vqa

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SURE-VQA: Systematic Understanding of Robustness Evaluation in Medical VQA Tasks

https://github.com/imoneoi/openchat

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OpenChat: Advancing Open-source Language Models with Mixed-Quality Data

https://github.com/imperialite/uniform-complexity-textgen

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Uniform Complexity for Text Generation

https://github.com/in-dist-adversarials/in_distribution_adversarial_examples

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In-distribution adversarial attacks on object recognition models using gradient-free search

https://github.com/infi-coder/infibench-evaluation-harness

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InfiBench: Evaluating the Question-Answering Capabilities of Code Large Language Models

https://github.com/infi-coder/infibench-evaluator

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InfiBench: Evaluating the Question-Answering Capabilities of Code Large Language Models

https://github.com/infly-ai/inf-mllm

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InfMLLM: A Unified Framework for Visual-Language Tasks

https://github.com/informagi/AQA

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AQA: Adaptive Question Answering in a Society of LLMs via Contextual Multi-Armed Bandit

https://github.com/inhwanbae/lmtrajectory

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Can Language Beat Numerical Regression? Language-Based Multimodal Trajectory Prediction

https://github.com/ink-usc/rationalemultirewarddistillation

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Tailoring Self-Rationalizers with Multi-Reward Distillation

https://github.com/insilicomedicine/nach0

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nach0: Multimodal Natural and Chemical Languages Foundation Model

https://github.com/intel-nrc-ecosystem/models

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A Long Short-Term Memory for AI Applications in Spike-based Neuromorphic Hardware

https://github.com/intel/intel-extension-for-transformers

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QuaLA-MiniLM: a Quantized Length Adaptive MiniLM

https://github.com/intell-sci-comput/slotpi

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SlotPi: Physics-informed Object-centric Reasoning Models

https://github.com/intellabs/model-compression-research-package

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Prune Once for All: Sparse Pre-Trained Language Models

https://github.com/intellabs/multimodal_cognitive_ai

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Analyzing Zero-Shot Abilities of Vision-Language Models on Video Understanding Tasks

https://github.com/intellabs/rag-fit

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SQuARE: Sequential Question Answering Reasoning Engine for Enhanced Chain-of-Thought in Large Language Models

https://github.com/intellabs/rag-retrieval-study

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Toward Optimal Search and Retrieval for RAG

https://github.com/intellabs/vl-interpret

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VL-InterpreT: An Interactive Visualization Tool for Interpreting Vision-Language Transformers

https://github.com/intellisensing/rs-agent

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RS-Agent: Automating Remote Sensing Tasks through Intelligent Agent

https://github.com/interaction-lab/hribench

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HRIBench: Benchmarking Vision-Language Models for Real-Time Human Perception in Human-Robot Interaction

https://github.com/interpretml/talktoebm

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Data Science with LLMs and Interpretable Models

https://github.com/intmegroup/lmm4lmm

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LMM4LMM: Benchmarking and Evaluating Large-multimodal Image Generation with LMMs

https://github.com/intmegroup/love

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LOVE: Benchmarking and Evaluating Text-to-Video Generation and Video-to-Text Interpretation

https://github.com/intuit-ai-research/knowledge-infused-ai/tree/main/synthetic-knowledge-ingestion

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Synthetic Knowledge Ingestion: Towards Knowledge Refinement and Injection for Enhancing Large Language Models

https://github.com/intuit/sac3

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SAC3: Reliable Hallucination Detection in Black-Box Language Models via Semantic-aware Cross-check Consistency

https://github.com/intuit/wmlm

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Unsupervised Improvement of Factual Knowledge in Language Models

https://github.com/invictus717/MiCo

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Explore the Limits of Omni-modal Pretraining at Scale

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