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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/paper-submission-rafa/diversification-code

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Result Diversification by Multi-objective Evolutionary Algorithms with Theoretical Guarantees

https://github.com/paperswithcode/galai

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Galactica: A Large Language Model for Science

https://github.com/para-lost/rvp

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Recursive Visual Programming

https://github.com/parameterlab/apricot

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Calibrating Large Language Models Using Their Generations Only

https://github.com/parinzee/seed-free-synthetic-instruct

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Seed-Free Synthetic Data Generation Framework for Instruction-Tuning LLMs: A Case Study in Thai

https://github.com/park-jungin/vlap

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Bridging Vision and Language Spaces with Assignment Prediction

https://github.com/parkervg/blendsql

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BlendSQL: A Scalable Dialect for Unifying Hybrid Question Answering in Relational Algebra

https://github.com/parthsarthi03/RAPTOR

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RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval

https://github.com/patil-suraj/question_generation

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Generative Data Augmentation using LLMs improves Distributional Robustness in Question Answering

https://github.com/patilli/vqa_benchmarking

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Beyond Accuracy: A Consolidated Tool for Visual Question Answering Benchmarking

https://github.com/patrickamadeus/vqa-nle-llava

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Towards Efficient and Robust VQA-NLE Data Generation with Large Vision-Language Models

https://github.com/patronus-ai/financebench

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FinanceBench: A New Benchmark for Financial Question Answering

https://github.com/pauamargant/hsr-graspsynth

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Sim2Real Transfer for Vision-Based Grasp Verification

https://github.com/paul-rottger/multiq

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Evaluating the Elementary Multilingual Capabilities of Large Language Models with MultiQ

https://github.com/pauldrm/discosqa

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Knowledge Base Question Answering for Space Debris Queries

https://github.com/paulgavrikov/vlm_shapebias

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Can We Talk Models Into Seeing the World Differently?

https://github.com/paullerner/viquae

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Multimodal Inverse Cloze Task for Knowledge-based Visual Question Answering

https://github.com/paulsubarna/core-tokenset

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Core Tokensets for Data-efficient Sequential Training of Transformers

https://github.com/pavel-blinov/RuMedBench

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RuMedBench: A Russian Medical Language Understanding Benchmark

https://github.com/pbhgit/mvcd

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Language Models Can See Better: Visual Contrastive Decoding For LLM Multimodal Reasoning

https://github.com/pds-dpo/pds-dpo

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Multimodal Preference Data Synthetic Alignment with Reward Model

https://github.com/pdx97/sbi-rag_neurips2024

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SBI-RAG: Enhancing Math Word Problem Solving for Students through Schema-Based Instruction and Retrieval-Augmented Generation

https://github.com/peanuth/familiarity-queryspec

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Characterising Topic Familiarity and Query Specificity Using Eye-Tracking Data

https://github.com/pengfeiliheu/m2i2

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Self-supervised vision-language pretraining for Medical visual question answering

https://github.com/penglinzhi/dygkt

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DyGKT: Dynamic Graph Learning for Knowledge Tracing

https://github.com/pengshancai/paniniqa

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PaniniQA: Enhancing Patient Education Through Interactive Question Answering

https://github.com/percyliang/sempre

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Compositional Semantic Parsing on Semi-Structured Tables

https://github.com/perevalov/qald_9_plus

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QALD-9-plus: A Multilingual Dataset for Question Answering over DBpedia and Wikidata Translated by Native Speakers

https://github.com/persiannlp/parsinlu

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ParsiNLU: A Suite of Language Understanding Challenges for Persian

https://github.com/petergriffinjin/search-r1

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Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning

https://github.com/peteriz/academic-budget-bert

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How to Train BERT with an Academic Budget

https://github.com/petrosrapto/pakton

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PAKTON: A Multi-Agent Framework for Question Answering in Long Legal Agreements

https://github.com/pharmolix/openbiomed

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BioMedGPT: Open Multimodal Generative Pre-trained Transformer for BioMedicine

https://github.com/phddamuge/UniRPG

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UniRPG: Unified Discrete Reasoning over Table and Text as Program Generation

https://github.com/philip-schrodt/ngec

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Creating Custom Event Data Without Dictionaries: A Bag-of-Tricks

https://github.com/philipmit/thread

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THREAD: Thinking Deeper with Recursive Spawning

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