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논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
The Generative AI Paradox on Evaluation: What It Can Solve, It May Not Evaluate
Attendre: Wait To Attend By Retrieval With Evicted Queries in Memory-Based Transformers for Long Context Processing
Efficient Transformer Knowledge Distillation: A Performance Review
Noisy Pair Corrector for Dense Retrieval
A Bias-Variance-Covariance Decomposition of Kernel Scores for Generative Models
Sorted LLaMA: Unlocking the Potential of Intermediate Layers of Large Language Models for Dynamic Inference
Generator-Retriever-Generator Approach for Open-Domain Question Answering
When to Read Documents or QA History: On Unified and Selective Open-domain QA
RFiD: Towards Rational Fusion-in-Decoder for Open-Domain Question Answering
Exploiting Abstract Meaning Representation for Open-Domain Question Answering
Allies: Prompting Large Language Model with Beam Search
Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedback
CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing
Dense Sparse Retrieval: Using Sparse Language Models for Inference Efficient Dense Retrieval
Quick Dense Retrievers Consume KALE: Post Training Kullback Leibler Alignment of Embeddings for Asymmetrical dual encoders
CLAM: Selective Clarification for Ambiguous Questions with Generative Language Models
Can Open-Domain QA Reader Utilize External Knowledge Efficiently like Humans?
FiE: Building a Global Probability Space by Leveraging Early Fusion in Encoder for Open-Domain Question Answering
Large Language Models Struggle to Learn Long-Tail Knowledge
Recitation-Augmented Language Models
Unsupervised Question Answering via Answer Diversifying
A Neural Corpus Indexer for Document Retrieval
SKILL: Structured Knowledge Infusion for Large Language Models
GNN-encoder: Learning a Dual-encoder Architecture via Graph Neural Networks for Dense Passage Retrieval
LinkBERT: Pretraining Language Models with Document Links
Deep Understanding based Multi-Document Machine Reading Comprehension
Text and Code Embeddings by Contrastive Pre-Training
Grow-and-Clip: Informative-yet-Concise Evidence Distillation for Answer Explanation
You Only Need One Model for Open-domain Question Answering
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
Attention-guided Generative Models for Extractive Question Answering
KG-FiD: Infusing Knowledge Graph in Fusion-in-Decoder for Open-Domain Question Answering
Adversarial Retriever-Ranker for dense text retrieval
Relation-Guided Pre-Training for Open-Domain Question Answering
Improving Unsupervised Question Answering via Summarization-Informed Question Generation
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