https://github.com/sunjiao123sun/llm-controlgen
공식 코드Evaluating Large Language Models on Controlled Generation Tasks
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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.
Evaluating Large Language Models on Controlled Generation Tasks
Do Text Simplification Systems Preserve Meaning? A Human Evaluation via Reading Comprehension
In-context Pretraining: Language Modeling Beyond Document Boundaries
Knowing More About Questions Can Help: Improving Calibration in Question Answering
Multi-Grained Query-Guided Set Prediction Network for Grounded Multimodal Named Entity Recognition
MAUD: An Expert-Annotated Legal NLP Dataset for Merger Agreement Understanding
Large Language Models for Power Scheduling: A User-Centric Approach
KoRC: Knowledge oriented Reading Comprehension Benchmark for Deep Text Understanding
Token-Level Contrastive Learning with Modality-Aware Prompting for Multimodal Intent Recognition
TEXTOIR: An Integrated and Visualized Platform for Text Open Intent Recognition
TEXTOIR: An Integrated and Visualized Platform for Text Open Intent Recognition
Lawformer: A Pre-trained Language Model for Chinese Legal Long Documents
MoEfication: Transformer Feed-forward Layers are Mixtures of Experts
Keep it Simple: Unsupervised Simplification of Multi-Paragraph Text
Understanding and Enhancing the Transferability of Jailbreaking Attacks
Parallel Instance Query Network for Named Entity Recognition
A Trigger-Sense Memory Flow Framework for Joint Entity and Relation Extraction
Differentiable Reasoning on Large Knowledge Bases and Natural Language
Differentiable Reasoning on Large Knowledge Bases and Natural Language
Differentiable Reasoning on Large Knowledge Bases and Natural Language
Understanding Model Robustness to User-generated Noisy Texts
Arithmetic-Based Pretraining -- Improving Numeracy of Pretrained Language Models
Arithmetic-Based Pretraining -- Improving Numeracy of Pretrained Language Models
Balancing Test Accuracy and Security in Computerized Adaptive Testing
Improving Reading Comprehension Question Generation with Data Augmentation and Overgenerate-and-rank
Deep Manifold Learning for Reading Comprehension and Logical Reasoning Tasks with Polytuplet Loss
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.