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Multiple Choice Question Answering (MCQA)
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A multiple-choice question (MCQ) is composed of two parts: a stem that identifies the question or problem, and a set of alternatives or possible answers that contain a key that is the best answer to the question, and a number of distractors that are plausible but incorrect answers to the question.
In a k-way MCQA task, a model is provided with a question q, a set of candidate options O = {O1, . . . , Ok}, and a supporting context for each option C = {C1, . . . , Ck}. The model needs to predict the correct answer option that is best supported by the given contexts.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/HaochenLiu2000/QAP
공식 코드Question-Aware Knowledge Graph Prompting for Enhancing Large Language Models
https://github.com/MedMCQA/MedMCQA
공식 코드MedMCQA : A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering
https://github.com/PluviophileYU/CVC-QA
공식 코드Counterfactual Variable Control for Robust and Interpretable Question Answering
https://github.com/VenkyMishra/legal_reasoning
공식 코드Investigating the Shortcomings of LLMs in Step-by-Step Legal Reasoning
https://github.com/VodLM/vod
공식 코드https://github.com/byu-pccl/leveraging-llms-for-mcqa
공식 코드Leveraging Large Language Models for Multiple Choice Question Answering
https://github.com/coastalcph/lex-glue
공식 코드LexGLUE: A Benchmark Dataset for Legal Language Understanding in English
https://github.com/dwq-vicki/dcqa
공식 코드Differentiating Choices via Commonality for Multiple-Choice Question Answering
https://github.com/epfllm/meditron
공식 코드MEDITRON-70B: Scaling Medical Pretraining for Large Language Models
https://github.com/facebookresearch/llama
공식 코드https://github.com/findzebra/fz-openqa
공식 코드https://github.com/ibm/indo-aryan-language-family-model
공식 코드https://github.com/ibraheem-moosa/xlm-indic
공식 코드https://github.com/jind11/MMM-MCQA
공식 코드MMM: Multi-stage Multi-task Learning for Multi-choice Reading Comprehension
https://github.com/mbzuai-nlp/multichoice2extractiveqa
공식 코드From Multiple-Choice to Extractive QA: A Case Study for English and Arabic
https://github.com/nbalepur/mcqa-artifacts
공식 코드Artifacts or Abduction: How Do LLMs Answer Multiple-Choice Questions Without the Question?
https://github.com/nijianmo/arc-etrr-code
공식 코드https://github.com/nju-websoft/genmc
공식 코드Clues Before Answers: Generation-Enhanced Multiple-Choice QA
https://github.com/nyu-mll/quality
공식 코드https://github.com/nyuolab/medg-krp
공식 코드https://github.com/paperswithcode/galai
공식 코드https://github.com/pharmolix/openbiomed
공식 코드BioMedGPT: Open Multimodal Generative Pre-trained Transformer for BioMedicine
https://github.com/qanastek/FrenchMedMCQA
공식 코드FrenchMedMCQA: A French Multiple-Choice Question Answering Dataset for Medical domain
https://github.com/thiliniiw/knowledgeprompts
공식 코드https://github.com/tjunlp-lab/m3ke
공식 코드https://github.com/vlievin/medical-reasoning
공식 코드https://github.com/yinzhu-quan/lm-evaluation-harness
공식 코드https://github.com/ys-zong/foolyourvllms
공식 코드Fool Your (Vision and) Language Model With Embarrassingly Simple Permutations
https://github.com/zefang-liu/adamole
공식 코드AdaMoLE: Fine-Tuning Large Language Models with Adaptive Mixture of Low-Rank Adaptation Experts
https://github.com/AI4Bharat/indic-bert
https://github.com/IBM/Dromedary
https://github.com/KiritoSSR/paddle_r2c
https://github.com/Lightning-AI/lit-gpt
https://github.com/TheShadow29/visual-commonsense-pytorch
https://github.com/ZHO9504/Select-Key-Terms-in-a-Question
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