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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/thunlp/OpenNRE

공식 코드

OpenNRE: An Open and Extensible Toolkit for Neural Relation Extraction

https://github.com/thunlp/adaptive-note

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Retriever-and-Memory: Towards Adaptive Note-Enhanced Retrieval-Augmented Generation

https://github.com/thunlp/document-plugin

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Plug-and-Play Document Modules for Pre-trained Models

https://github.com/thunlp/dpt

공식 코드

Prompt Tuning for Discriminative Pre-trained Language Models

https://github.com/thunlp/embodiedeval

공식 코드

EmbodiedEval: Evaluate Multimodal LLMs as Embodied Agents

https://github.com/thunlp/falseqa

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Won't Get Fooled Again: Answering Questions with False Premises

https://github.com/thunlp/fastfid

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FastFiD: Improve Inference Efficiency of Open Domain Question Answering via Sentence Selection

https://github.com/thunlp/kbalign

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KBAlign: Efficient Self Adaptation on Specific Knowledge Bases

https://github.com/thunlp/webcpm

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WebCPM: Interactive Web Search for Chinese Long-form Question Answering

https://github.com/tianhongzxy/qaap

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Question Answering as Programming for Solving Time-Sensitive Questions

https://github.com/tiantiangao7/kalm-qa

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Querying Knowledge via Multi-Hop English Questions

https://github.com/tianyao-aka/rapl

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Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering

https://github.com/tianyin123/med-refl

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Med-REFL: Medical Reasoning Enhancement via Self-Corrected Fine-grained Reflection

https://github.com/tianyu-z/vcr

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VCR: A Task for Pixel-Level Complex Reasoning in Vision Language Models via Restoring Occluded Text

https://github.com/tibhannover/patent-figure-classification

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Patent Figure Classification using Large Vision-language Models

https://github.com/tigerchen52/qrneuron

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Identifying Query-Relevant Neurons in Large Language Models for Long-Form Texts

https://github.com/timericschwabe/q-nl-verifier

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Q-NL Verifier: Leveraging Synthetic Data for Robust Knowledge Graph Question Answering

https://github.com/timhartill/unseen_questions

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Teaching Smaller Language Models To Generalise To Unseen Compositional Questions

https://github.com/timmy-chan/miss

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MISS: A Generative Pretraining and Finetuning Approach for Med-VQA

https://github.com/tingyu215/ts-llava

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TS-LLaVA: Constructing Visual Tokens through Thumbnail-and-Sampling for Training-Free Video Large Language Models

https://github.com/tjunlp-lab/m3ke

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M3KE: A Massive Multi-Level Multi-Subject Knowledge Evaluation Benchmark for Chinese Large Language Models

https://github.com/tjvsonsbeek/open-ended-medical-vqa

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Open-Ended Medical Visual Question Answering Through Prefix Tuning of Language Models

https://github.com/tloen/alpaca-lora

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RET-LLM: Towards a General Read-Write Memory for Large Language Models

https://github.com/toloka/wsdmcup2023

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Toloka Visual Question Answering Benchmark

https://github.com/tomohiro-sawada/cs7643-final-project

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Towards a Unified Multimodal Reasoning Framework

https://github.com/toneli/petailor-for-bio-triple-extraction

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BiomedRAG: A Retrieval Augmented Large Language Model for Biomedicine

https://github.com/tong-zeng/dvbench

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Are Vision LLMs Road-Ready? A Comprehensive Benchmark for Safety-Critical Driving Video Understanding

https://github.com/tongjinlp/acc

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Correct after Answer: Enhancing Multi-Span Question Answering with Post-Processing Method

https://github.com/tongyi-zhiwen/qwenlong-l1

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QwenLong-L1: Towards Long-Context Large Reasoning Models with Reinforcement Learning

https://github.com/tongzhou21/CogMG

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CogMG: Collaborative Augmentation Between Large Language Model and Knowledge Graph

https://github.com/tonyby/myth-qa

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MythQA: Query-Based Large-Scale Check-Worthy Claim Detection through Multi-Answer Open-Domain Question Answering

https://github.com/tonyby/ramqa

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RAMQA: A Unified Framework for Retrieval-Augmented Multi-Modal Question Answering

https://github.com/topdu/openocr

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Instruction-Guided Scene Text Recognition

https://github.com/tossowski/multimodalpromptretrieval

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Multimodal Prompt Retrieval for Generative Visual Question Answering

https://github.com/towardsautonomy/qagan

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QAGAN: Adversarial Approach To Learning Domain Invariant Language Features

https://github.com/tpfi22/spectral-and-combinatorial

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Weisfeiler and Leman Go Infinite: Spectral and Combinatorial Pre-Colorings

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.