https://github.com/obss/turkish-question-generation
공식 코드Automated question generation and question answering from Turkish texts
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/ocastel/exact-extract
공식 코드How Optimal is Greedy Decoding for Extractive Question Answering?
https://github.com/oceanntwt/era-cot
공식 코드ERA-CoT: Improving Chain-of-Thought through Entity Relationship Analysis
https://github.com/oceanntwt/spa
공식 코드https://github.com/oceanypt/coldqa-tta
공식 코드Robust Question Answering against Distribution Shifts with Test-Time Adaptation: An Empirical Study
https://github.com/oceanypt/multi-source-tta
공식 코드Multi-Source Test-Time Adaptation as Dueling Bandits for Extractive Question Answering
https://github.com/ofa-sys/gsm8k-screl
공식 코드MuggleMath: Assessing the Impact of Query and Response Augmentation on Math Reasoning
https://github.com/ofirpress/self-ask
공식 코드Measuring and Narrowing the Compositionality Gap in Language Models
https://github.com/omossad/fat-albert
공식 코드FAT ALBERT: Finding Answers in Large Texts using Semantic Similarity Attention Layer based on BERT
https://github.com/omrikeren/parashoot
공식 코드https://github.com/omron-sinicx/whereistheanswer
공식 코드Where is the answer? Investigating Positional Bias in Language Model Knowledge Extraction
https://github.com/oniani/covid-19-chatbot
공식 코드A Qualitative Evaluation of Language Models on Automatic Question-Answering for COVID-19
https://github.com/oooranz/baby-cothought
공식 코드Baby's CoThought: Leveraging Large Language Models for Enhanced Reasoning in Compact Models
https://github.com/open-compass/ada-leval
공식 코드Ada-LEval: Evaluating long-context LLMs with length-adaptable benchmarks
https://github.com/open-compass/anah
공식 코드ANAH: Analytical Annotation of Hallucinations in Large Language Models
https://github.com/open-dataflow/vts-v
공식 코드Multi-Step Visual Reasoning with Visual Tokens Scaling and Verification
https://github.com/open-mmlab/mmdetection
공식 코드https://github.com/open-sciencelab/graphgen
공식 코드GraphGen: Enhancing Supervised Fine-Tuning for LLMs with Knowledge-Driven Synthetic Data Generation
https://github.com/open3da/ll3da
공식 코드LL3DA: Visual Interactive Instruction Tuning for Omni-3D Understanding, Reasoning, and Planning
https://github.com/openai/evals
공식 코드https://github.com/openai/following-instructions-human-feedback
공식 코드Training language models to follow instructions with human feedback
https://github.com/openai/gpt-3
공식 코드https://github.com/openai/prm800k
공식 코드https://github.com/openai/sparse_attention
공식 코드https://github.com/openbiolink/thoughtsource
공식 코드ThoughtSource: A central hub for large language model reasoning data
https://github.com/openbmb/rageval
공식 코드RAGEval: Scenario Specific RAG Evaluation Dataset Generation Framework
https://github.com/opencausalab/more
공식 코드Quantifying and Mitigating Unimodal Biases in Multimodal Large Language Models: A Causal Perspective
https://github.com/opendatalab/craft
공식 코드https://github.com/opendatalab/h2rsvlm
공식 코드VHM: Versatile and Honest Vision Language Model for Remote Sensing Image Analysis
https://github.com/opendatalab/vhm
공식 코드VHM: Versatile and Honest Vision Language Model for Remote Sensing Image Analysis
https://github.com/opendrivelab/drivelm
공식 코드https://github.com/opengvlab/all-seeing
공식 코드The All-Seeing Project: Towards Panoptic Visual Recognition and Understanding of the Open World
https://github.com/opengvlab/ask-anything
공식 코드https://github.com/opengvlab/internvideo
공식 코드InternVideo: General Video Foundation Models via Generative and Discriminative Learning
https://github.com/opengvlab/internvideo2
공식 코드InternVideo2: Scaling Foundation Models for Multimodal Video Understanding
https://github.com/opengvlab/llmprune-besa
공식 코드BESA: Pruning Large Language Models with Blockwise Parameter-Efficient Sparsity Allocation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.