https://github.com/facebookresearch/layerskip
공식 코드LayerSkip: Enabling Early Exit Inference and Self-Speculative Decoding
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Most current question answering datasets frame the task as reading comprehension where the question is about a paragraph or document and the answer often is a span in the document.
Some specific tasks of reading comprehension include multi-modal machine reading comprehension and textual machine reading comprehension, among others. In the literature, machine reading comprehension can be divide into four categories: cloze style, multiple choice, span prediction, and free-form answer. Read more about each category here.
Benchmark datasets used for testing a model's reading comprehension abilities include MovieQA, ReCoRD, and RACE, among others.
The Machine Reading group at UCL also provides an overview of reading comprehension tasks.
Figure source: A Survey on Machine Reading Comprehension: Tasks, Evaluation Metrics and Benchmark Datasets
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
LayerSkip: Enabling Early Exit Inference and Self-Speculative Decoding
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Large-scale Multi-granular Concept Extraction Based on Machine Reading Comprehension
Analyzing Multi-Task Learning for Abstractive Text Summarization
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Evaluating LLMs for Targeted Concept Simplification for Domain-Specific Texts
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Pretraining with Contrastive Sentence Objectives Improves Discourse Performance of Language Models
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JECC: Commonsense Reasoning Tasks Derived from Interactive Fictions
GraFormer: Graph Convolution Transformer for 3D Pose Estimation
On the Impact of Speech Recognition Errors in Passage Retrieval for Spoken Question Answering
Improving Visual Commonsense in Language Models via Multiple Image Generation
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HAE-RAE Bench: Evaluation of Korean Knowledge in Language Models
ConditionalQA: A Complex Reading Comprehension Dataset with Conditional Answers
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출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.