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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/wayveai/lingoqa

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LingoQA: Visual Question Answering for Autonomous Driving

https://github.com/wbw520/direct

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DiReCT: Diagnostic Reasoning for Clinical Notes via Large Language Models

https://github.com/webis-de/acl-22

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Clickbait Spoiling via Question Answering and Passage Retrieval

https://github.com/webis-de/arxiv24-ranking-generated-answers

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Ranking Generated Answers: On the Agreement of Retrieval Models with Humans on Consumer Health Questions

https://github.com/weiyifan1023/MVGE

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Multi-View Graph Representation Learning for Answering Hybrid Numerical Reasoning Question

https://github.com/wenchuan-zhang/patho-r1

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Patho-R1: A Multimodal Reinforcement Learning-Based Pathology Expert Reasoner

https://github.com/wendywangwwt/zero-shot-complex-question-answering-on-long-scientific-documents

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Zero-Shot Complex Question-Answering on Long Scientific Documents

https://github.com/wengsyx/controllm

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ControlLM: Crafting Diverse Personalities for Language Models

https://github.com/wengsyx/hot

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Large Language Models Need Holistically Thought in Medical Conversational QA

https://github.com/wengsyx/lingyi

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LingYi: Medical Conversational Question Answering System based on Multi-modal Knowledge Graphs

https://github.com/wenhuang2000/vhtest

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Visual Hallucinations of Multi-modal Large Language Models

https://github.com/wenhuchen/HybridQA

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HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data

https://github.com/wenhuchen/OTT-QA

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Open Question Answering over Tables and Text

https://github.com/wenhuchen/theoremqa

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TheoremQA: A Theorem-driven Question Answering dataset

https://github.com/wenjiazh/newsquote

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NewsQuote: A Dataset Built on Quote Extraction and Attribution for Expert Recommendation in Fact-Checking

https://github.com/wenjinw/latin-prompt

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Layout and Task Aware Instruction Prompt for Zero-shot Document Image Question Answering

https://github.com/wenkehuang/awesome-mllm-tuning

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Keeping Yourself is Important in Downstream Tuning Multimodal Large Language Model

https://github.com/wenshuaizhao/spmarl

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Learning Progress Driven Multi-Agent Curriculum

https://github.com/wentao-xu/kge-cl

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KGE-CL: Contrastive Learning of Tensor Decomposition Based Knowledge Graph Embeddings

https://github.com/wenting-zhao/couldask

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I Could've Asked That: Reformulating Unanswerable Questions

https://github.com/wenwei202/smoothout

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SmoothOut: Smoothing Out Sharp Minima to Improve Generalization in Deep Learning

https://github.com/wenzhengzhang/entqa

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EntQA: Entity Linking as Question Answering

https://github.com/westlake-autolab/biokgbench.github.io

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BioKGBench: A Knowledge Graph Checking Benchmark of AI Agent for Biomedical Science

https://github.com/wgantt/eae-transfer

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Small Models Are (Still) Effective Cross-Domain Argument Extractors

https://github.com/wgcyeo/UniversalRAG

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UniversalRAG: Retrieval-Augmented Generation over Corpora of Diverse Modalities and Granularities

https://github.com/wglab/bioformer

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Bioformer: an efficient transformer language model for biomedical text mining

https://github.com/whb139426/gcg

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Weakly Supervised Gaussian Contrastive Grounding with Large Multimodal Models for Video Question Answering

https://github.com/whb139426/qa-prompts-eccv-24

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Q&A Prompts: Discovering Rich Visual Clues through Mining Question-Answer Prompts for VQA requiring Diverse World Knowledge

https://github.com/whu-zqh/chatgpt-vs.-bert

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Can ChatGPT Understand Too? A Comparative Study on ChatGPT and Fine-tuned BERT

https://github.com/whu-zqh/dup

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Achieving >97% on GSM8K: Deeply Understanding the Problems Makes LLMs Better Solvers for Math Word Problems

https://github.com/whwu95/freeva

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FreeVA: Offline MLLM as Training-Free Video Assistant

https://github.com/wikipedia2vec/wikipedia2vec

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Neural Attentive Bag-of-Entities Model for Text Classification

https://github.com/wilcoxeg/neural-networks-read-times

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On the Predictive Power of Neural Language Models for Human Real-Time Comprehension Behavior

https://github.com/williamliujl/Qilin-Med

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Qilin-Med: Multi-stage Knowledge Injection Advanced Medical Large Language Model

https://github.com/williamliujl/cmexam

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Benchmarking Large Language Models on CMExam -- A Comprehensive Chinese Medical Exam Dataset

https://github.com/wimh966/QDrop

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QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization

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