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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/hritikbansal/videocon

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VideoCon: Robust Video-Language Alignment via Contrast Captions

https://github.com/hrl-mike/pitvqa-plus

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PitVQA++: Vector Matrix-Low-Rank Adaptation for Open-Ended Visual Question Answering in Pituitary Surgery

https://github.com/hs-yn/panoavqa

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Pano-AVQA: Grounded Audio-Visual Question Answering on 360$^\circ$ Videos

https://github.com/hshjerry/videoespresso

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VideoEspresso: A Large-Scale Chain-of-Thought Dataset for Fine-Grained Video Reasoning via Core Frame Selection

https://github.com/hsiangwei0903/spatialagent

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Warehouse Spatial Question Answering with LLM Agent

https://github.com/htsucml/VQAG

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End-to-End Video Question-Answer Generation with Generator-Pretester Network

https://github.com/huangyangyi/tech

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TeCH: Text-guided Reconstruction of Lifelike Clothed Humans

https://github.com/huawei-noah/Pretrained-Language-Model/tree/master/TinyBERT

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TinyBERT: Distilling BERT for Natural Language Understanding

https://github.com/hucvl/craft

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CRAFT: A Benchmark for Causal Reasoning About Forces and inTeractions

https://github.com/huggingface/nn_pruning

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Block Pruning For Faster Transformers

https://github.com/huggingface/swift-coreml-transformers

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DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

https://github.com/huggingface/transformers

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DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

https://github.com/huggingface/transformers/blob/main/src/transformers/models/squeezebert/modeling_squeezebert.py

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SqueezeBERT: What can computer vision teach NLP about efficient neural networks?

https://github.com/huggingface/transformers/tree/master/examples/research_projects/rag-end2end-retriever

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Fine-tune the Entire RAG Architecture (including DPR retriever) for Question-Answering

https://github.com/hugochan/BAMnet

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Bidirectional Attentive Memory Networks for Question Answering over Knowledge Bases

https://github.com/hugochan/PFoodReq

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Personalized Food Recommendation as Constrained Question Answering over a Large-scale Food Knowledge Graph

https://github.com/huminghao16/MTMSN

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A Multi-Type Multi-Span Network for Reading Comprehension that Requires Discrete Reasoning

https://github.com/huminghao16/RE3QA

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Retrieve, Read, Rerank: Towards End-to-End Multi-Document Reading Comprehension

https://github.com/huntila/scholarly-kgqa

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Leveraging LLMs in Scholarly Knowledge Graph Question Answering

https://github.com/husseinmozannar/SOQAL

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Neural Arabic Question Answering

https://github.com/hwanchang00/CoPriva

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Keep Security! Benchmarking Security Policy Preservation in Large Language Model Contexts Against Indirect Attacks in Question Answering

https://github.com/hwanheelee1993/KPQA

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KPQA: A Metric for Generative Question Answering Using Keyphrase Weights

https://github.com/hwanheelee1993/qace

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QACE: Asking Questions to Evaluate an Image Caption

https://github.com/hwy9855/multihopqa-reasoning

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Masking in Multi-hop QA: An Analysis of How Language Models Perform with Context Permutation

https://github.com/hxyz-123/reasoningocr

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Reasoning-OCR: Can Large Multimodal Models Solve Complex Logical Reasoning Problems from OCR Cues?

https://github.com/hybridNLP/look_read_and_enrich

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Look, Read and Enrich. Learning from Scientific Figures and their Captions

https://github.com/hyeonjeongha/mm-poisonrag

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MM-PoisonRAG: Disrupting Multimodal RAG with Local and Global Poisoning Attacks

https://github.com/hyintell/retrievalqa

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RetrievalQA: Assessing Adaptive Retrieval-Augmented Generation for Short-form Open-Domain Question Answering

https://github.com/hyn2028/llm-cxr

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LLM-CXR: Instruction-Finetuned LLM for CXR Image Understanding and Generation

https://github.com/hyounghk/VideoQADenseCapFrameGate-ACL2020

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Dense-Caption Matching and Frame-Selection Gating for Temporal Localization in VideoQA

https://github.com/hysonlab/videberta

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ViDeBERTa: A powerful pre-trained language model for Vietnamese

https://github.com/hyu-zhang/hcqa

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HCQA @ Ego4D EgoSchema Challenge 2024

https://github.com/hyu-zhang/itr

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Uncovering Hidden Connections: Iterative Search and Reasoning for Video-grounded Dialog

https://github.com/i-machine-think/diagnnose

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Analysing Neural Language Models: Contextual Decomposition Reveals Default Reasoning in Number and Gender Assignment

https://github.com/iaar-shanghai/crud_rag

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CRUD-RAG: A Comprehensive Chinese Benchmark for Retrieval-Augmented Generation of Large Language Models

https://github.com/iaar-shanghai/pgrag

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Empowering Large Language Models to Set up a Knowledge Retrieval Indexer via Self-Learning

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