3 accepted papers
The alignment of large language models (LLMs) is crucial for generating helpful and harmless content. Existing approaches leverage preference-based human feedback data to learn the reward function and align the LLM with the feedback data. However, these approaches focus on modeling the reward differ…
Retrieval-Augmented Generation (RAG) plays a critical role in mitigating hallucinations and improving factual accuracy for Large Language Models (LLMs). While dynamic retrieval techniques aim to determine retrieval timing and content based on model intrinsic needs, existing approaches struggle to ge
Cross-Lingual Summarization (CLS) involves generating a summary for a given document in another language. Most of the existing approaches adopt multi-task training and knowledge distillation, which increases the training cost and improves the performance of CLS tasks intuitively but unexplainably. I…