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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/codexxxl/GraphVQA

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GraghVQA: Language-Guided Graph Neural Networks for Graph-based Visual Question Answering

https://github.com/codezakh/SIMLA

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Single-Stream Multi-Level Alignment for Vision-Language Pretraining

https://github.com/codezakh/seltda

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Q: How to Specialize Large Vision-Language Models to Data-Scarce VQA Tasks? A: Self-Train on Unlabeled Images!

https://github.com/cods-gcs/chatty-gen

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Dialogue Benchmark Generation from Knowledge Graphs with Cost-Effective Retrieval-Augmented LLMs

https://github.com/cods-gcs/kgqan

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A Universal Question-Answering Platform for Knowledge Graphs

https://github.com/cofe-ai/msg

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Masked Structural Growth for 2x Faster Language Model Pre-training

https://github.com/cognizant-ai-labs/semantic-density-paper

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Semantic Density: Uncertainty Quantification for Large Language Models through Confidence Measurement in Semantic Space

https://github.com/coisini-glenda/wsdan-for-medical-visual-question-answering

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A Dual-Attention Learning Network with Word and Sentence Embedding for Medical Visual Question Answering

https://github.com/coldmist-lu/erroranalysis_prompt

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Error Analysis Prompting Enables Human-Like Translation Evaluation in Large Language Models

https://github.com/coldseal/clinicr

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Few shot chain-of-thought driven reasoning to prompt LLMs for open ended medical question answering

https://github.com/colinlu50/evade-gpt-detector

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Large Language Models can be Guided to Evade AI-Generated Text Detection

https://github.com/colinzhaoust/fact-and-reflection

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Fact-and-Reflection (FaR) Improves Confidence Calibration of Large Language Models

https://github.com/collin-burns/discovering_latent_knowledge

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Discovering Latent Knowledge in Language Models Without Supervision

https://github.com/colored-dye/truthfulness_probe_generalization

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Probing the Geometry of Truth: Consistency and Generalization of Truth Directions in LLMs Across Logical Transformations and Question Answering Tasks

https://github.com/compai-lab/miccai-2024-junli

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Language Models Meet Anomaly Detection for Better Interpretability and Generalizability

https://github.com/computer-vision-in-the-wild/cvinw_readings

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Vision-Language Pre-training: Basics, Recent Advances, and Future Trends

https://github.com/contrastive/freevideollm

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Free Video-LLM: Prompt-guided Visual Perception for Efficient Training-free Video LLMs

https://github.com/control-xl/medical-vision-langauge-transformer

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Multi-modal Pre-training for Medical Vision-language Understanding and Generation: An Empirical Study with A New Benchmark

https://github.com/convergence-ai/lm2

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LM2: Large Memory Models

https://github.com/cooelf/AwesomeMRC

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Retrospective Reader for Machine Reading Comprehension

https://github.com/cooelf/OneshotQA

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One-shot Learning for Question-Answering in Gaokao History Challenge

https://github.com/cooelf/SemBERT

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Semantics-aware BERT for Language Understanding

https://github.com/cooelf/compassmtl

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Task Compass: Scaling Multi-task Pre-training with Task Prefix

https://github.com/cooldawnant/infochartqa

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InfoChartQA: A Benchmark for Multimodal Question Answering on Infographic Charts

https://github.com/copenlu/X-MAML

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Zero-Shot Cross-Lingual Transfer with Meta Learning

https://github.com/cpystan/wsi-vqa

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WSI-VQA: Interpreting Whole Slide Images by Generative Visual Question Answering

https://github.com/cristianocapone/biodreaming

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Towards biologically plausible Dreaming and Planning in recurrent spiking networks

https://github.com/csarkar/tage

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tagE: Enabling an Embodied Agent to Understand Human Instructions

https://github.com/csarron/sustainlp2020-energy

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Towards Accurate and Reliable Energy Measurement of NLP Models

https://github.com/cse-ai-lab/RealCQA

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RealCQA: Scientific Chart Question Answering as a Test-bed for First-Order Logic

https://github.com/csebuetnlp/banglabert

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BanglaBERT: Language Model Pretraining and Benchmarks for Low-Resource Language Understanding Evaluation in Bangla

https://github.com/csinva/interpretable-embeddings

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Crafting Interpretable Embeddings by Asking LLMs Questions

https://github.com/csjywu1/adkgd

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ADKGD: Anomaly Detection in Knowledge Graphs with Dual-Channel Training

https://github.com/csuhan/onellm

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OneLLM: One Framework to Align All Modalities with Language

https://github.com/curai/curai-research

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DERA: Enhancing Large Language Model Completions with Dialog-Enabled Resolving Agents

https://github.com/cuthbertcai/demovlp

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Revitalize Region Feature for Democratizing Video-Language Pre-training of Retrieval

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