Paper-Conference

QUITO-X: A New Perspective on Context Compression from the Information Bottleneck Theory

Generative LLM have achieved remarkable success in various industrial applications, owing to their promising In-Context Learning capabilities. However, the issue of long context in …

Yihang Wang

• Aug 1, 2025 • 1 min read

MDPO: Customized Direct Preference Optimization with a Metric-based Sampler for Question and Answer Generation

With the extensive use of large language models, automatically generating QA datasets for domain-specific fine-tuning has become crucial. However, considering the multifaceted …

Yihang Wang

• Jan 1, 2025 • 1 min read

QUITO: Accelerating Long-Context Reasoning through Query-Guided Context Compression

In-context learning (ICL) capabilities are foundational to the success of large language models (LLMs). Recently, context compression has attracted growing interest since it can …

Wenshan Wang

• Jul 1, 2024 • 1 min read