5 papers
CoDA: Towards Effective Cross-domain Knowledge Transfer via CoT-guided Domain Adaptation
Jianzhi Yan, Le Liu, Buzhou Tang +3
Large language models (LLMs) have achieved substantial advances in logical reasoning, yet they continue to lag behind human-level performance. In-context learning provides a viable…
Reason Analogically via Cross-domain Prior Knowledge: An Empirical Study of Cross-domain Knowledge Transfer for In-Context Learning
Le Liu, Zhiming Li, Jianzhi Yan +7
Despite its success, existing in-context learning (ICL) relies on in-domain expert demonstrations, limiting its applicability when expert annotations are scarce. We posit that diff…
Towards Effective In-context Cross-domain Knowledge Transfer via Domain-invariant-neurons-based Retrieval
Jianzhi Yan, Zhiming Li, Le Liu +6
Large language models (LLMs) have made notable progress in logical reasoning, yet still fall short of human-level performance. Current boosting strategies rely on expert-crafted in…
Towards Efficient CoT Distillation: Self-Guided Rationale Selector for Better Performance with Fewer Rationales
Jianzhi Yan, Le Liu, Youcheng Pan +3
Chain-of-thought (CoT) distillation aims to enhance small language models' (SLMs) reasoning by transferring multi-step reasoning capability from the larger teacher models. However,…
From Long to Lean: Performance-aware and Adaptive Chain-of-Thought Compression via Multi-round Refinement
Jianzhi Yan, Le Liu, Youcheng Pan +4
Chain-of-Thought (CoT) reasoning improves performance on complex tasks but introduces significant inference latency due to verbosity. We propose Multiround Adaptive Chain-of-Though…