Papers with Code 아카이브
Question Answering
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 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/sunnynexus/search-o1
공식 코드https://github.com/superagi/veagle
공식 코드https://github.com/superyue72/OptLLM
공식 코드OptLLM: Optimal Assignment of Queries to Large Language Models
https://github.com/suu990901/chatgpt-comparison-detection-HC3-Plus
공식 코드HC3 Plus: A Semantic-Invariant Human ChatGPT Comparison Corpus
https://github.com/suvodeep90/semi_supervised_methods
공식 코드When Less is More: On the Value of "Co-training" for Semi-Supervised Software Defect Predictors
https://github.com/svaishnavi411/multimodal-recurrence
공식 코드Multimodal fusion of imaging and genomics for lung cancer recurrence prediction
https://github.com/svakulenk0/KBQA
공식 코드Message Passing for Complex Question Answering over Knowledge Graphs
https://github.com/svakulenk0/ODExploration_data
공식 코드https://github.com/svakulenk0/QRQA
공식 코드https://github.com/svakulenk0/cast_evaluation
공식 코드A Comparison of Question Rewriting Methods for Conversational Passage Retrieval
https://github.com/swadesh06/revisting_cropa
공식 코드https://github.com/swaroop-nath/rl-qfs
공식 코드Reinforcement Replaces Supervision: Query focused Summarization using Deep Reinforcement Learning
https://github.com/swarupbehera/mAVQA
공식 코드https://github.com/sweta20/ats-eval
공식 코드Do Text Simplification Systems Preserve Meaning? A Human Evaluation via Reading Comprehension
https://github.com/swj0419/DESCGEN
공식 코드DESCGEN: A Distantly Supervised Dataset for Generating Abstractive Entity Descriptions
https://github.com/swj0419/knn_prompt
공식 코드https://github.com/swordlidev/evaluation-multimodal-llms-survey
공식 코드https://github.com/sycny/rae
공식 코드Retrieval-enhanced Knowledge Editing in Language Models for Multi-Hop Question Answering
https://github.com/sylinrl/truthfulqa
공식 코드https://github.com/sylphai-inc/adalflow
공식 코드https://github.com/synlp/chimed-gpt
공식 코드https://github.com/syxu828/QuestionAnsweringOverFB
공식 코드Question Answering on Freebase via Relation Extraction and Textual Evidence
https://github.com/szhang42/Calibration_qa
공식 코드Knowing More About Questions Can Help: Improving Calibration in Question Answering
https://github.com/szhang42/Contextual_dropout_release
공식 코드Contextual Dropout: An Efficient Sample-Dependent Dropout Module
https://github.com/szhang42/alignment_attention
공식 코드https://github.com/szu-tera/rankedvotingsc
공식 코드Ranked Voting based Self-Consistency of Large Language Models
https://github.com/szzexpoi/AiR
공식 코드https://github.com/t-montes/viqagent
공식 코드ViQAgent: Zero-Shot Video Question Answering via Agent with Open-Vocabulary Grounding Validation
https://github.com/t-sciq/t-sciq
공식 코드https://github.com/tLabruna/Adapt-LLM
공식 코드When to Retrieve: Teaching LLMs to Utilize Information Retrieval Effectively
https://github.com/taco-group/autotrust
공식 코드AutoTrust: Benchmarking Trustworthiness in Large Vision Language Models for Autonomous Driving
https://github.com/taco-group/re-align
공식 코드Re-Align: Aligning Vision Language Models via Retrieval-Augmented Direct Preference Optimization
https://github.com/tae2hee/coqah
공식 코드https://github.com/taejoo-ahn/fdr_bayes_figures
공식 코드Near-optimal multiple testing in Bayesian linear models with finite-sample FDR control
https://github.com/taekb/eval-medical-dapt
공식 코드Medical Adaptation of Large Language and Vision-Language Models: Are We Making Progress?
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.