Rui Ling

Recent preference optimization algorithms such as Direct Preference Optimization (DPO) have become prevalent for aligning large language models (LLMs) with human preferences. FocalPO improves upon DPO by introducing a modulating factor that down-weighs misranked preference pairs. However, using a...

Scaling test-time compute through extended chains of thought has become a dominant paradigm for improving large language model reasoning. However, existing research implicitly assumes that longer thinking always yields better results. This assumption remains largely unexamined. We systematically...

Large Language Models (LLMs) are known to hallucinate, generating non-factual outputs that undermine user trust. Recent ensemble-based approaches leverage uncertainty estimation to select among multiple LLM responses, achieving promising results in hallucination mitigation. However, these methods...

Retrieval-augmented generation (RAG) has emerged as a promising solution to enhance the reliability of large language models (LLMs) with external knowledge. Existing RAG methods operate in explicit representation spaces: in-context methods inject knowledge through text tokens in the input, while...

Retrieval-augmented generation (RAG) has emerged as a promising paradigm to enhance Large Language Models (LLMs) with external knowledge, effectively mitigating hallucinations and broadening the model's knowledge coverage. Despite recent advances, existing RAG methods fundamentally assume static...

Knowledge distillation (KD) from cross-encoder teachers is a widely adopted technique for training effective neural retrieval models. However, recent studies have revealed a puzzling phenomenon: while retrieval models trained with contrastive loss (CL) exhibit clear scaling behavior with larger l...