yichong huang

Retrieval-Augmented Generation (RAG) significantly improves the factual accuracy and generation quality of large language models by incorporating external knowledge. However, in multilingual settings, RAG systems suffer from severe language preference. On the one hand, the retrieval stage is sens...

Languages encode distinct abstractions and inductive priors, yet most large language models (LLMs) overlook this diversity by reasoning in a single dominant language. In this work, we introduce x1, a family of reasoning models that can adaptively reason in an advantageous language on a per-instan...

Merging a large number of low-rank adaptations (LoRAs) is a key technology for enhancing the integration and deployment efficiency of large language models (LLMs). However, current general model merging methods are prone to “parameter interference” problem, and this issue is especially pronounced...