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Found 9 papers, 4 papers with code
Fine-Tuning Language Models with Reward Learning on Policy
RLHF contains three steps, i. e., human preference collecting, reward learning, and policy optimization, which are usually performed serially.
Scaling Data Diversity for Fine-Tuning Language Models in Human Alignment
Additionally, the concept of diversity for prompts can be more complex than responses that are typically quantified by single digits.
Exploring Large Language Models for Multi-Modal Out-of-Distribution Detection
Out-of-distribution (OOD) detection is essential for reliable and trustworthy machine learning.
Long-Tailed Question Answering in an Open World
A retrieve-then-rerank frame is further introduced to select in-context examples, which guild the LM to generate text that express knowledge for QA tasks.
Domain Incremental Lifelong Learning in an Open World
Specifically, we dedicate task-level prompts to capture task-specific knowledge to retain high LL performances and maintain instance-level prompts to learn knowledge shared across input samples to improve the model's generalization performance.
Out-of-Domain Intent Detection Considering Multi-Turn Dialogue Contexts
Out-of-Domain (OOD) intent detection is vital for practical dialogue systems, and it usually requires considering multi-turn dialogue contexts.
A Survey on Out-of-Distribution Detection in NLP
Out-of-distribution (OOD) detection is essential for the reliable and safe deployment of machine learning systems in the real world.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
+1
Estimating Soft Labels for Out-of-Domain Intent Detection
Out-of-Domain (OOD) intent detection is important for practical dialog systems.
Automated Curriculum Learning for Turn-level Spoken Language Understanding with Weak Supervision
The RBSMA algorithm improves the test set accuracy by 7. 8% relative compared to the standard beam search.
