How can we share parameters within large language models to significantly reduce memory costs while preserving accuracy? While parameter sharing is a promising solution to the memory overhead of large language models, existing methods rely on naive grouping and fail to correct sharing-induced discrepancies. We propose an accurate and efficient parameter sharing framework, SharVeT (Similarity-aware sharing with Vector-based Tuning), which performs similarity-based grouping to ensure accurate sharing, allocates parameters adaptively to preserve diversity within each group, and applies lightweight refinement with knowledge distillation to correct sharing-induced discrepancies. Experiments show that SharVeT outperforms existing sharing methods, achieving up to 32.1% lower perplexity and 23.3% higher few-shot reasoning accuracy.

How can we quantize large language models while preserving accuracy? Quantization is essential for deploying large language models (LLMs) efficiently. Binary-coding quantization (BCQ) and uniform quantization (UQ) are promising quantization schemes that have strong expressiveness and optimizability, respectively. However, neither scheme leverages both advantages. In this paper, we propose UniQuan_F (Unified Quantization with Flexible Mapping), an accurate quantization method for LLMs. UniQuan_F harnesses both strong expressiveness and optimizability by unifying the flexible mapping technique in UQ and BCQ’s non-uniform quantization levels. We propose unified initialization, and local and periodic mapping techniques to optimize the parameters in UniQuan_F precisely. After optimization, our unification theorem removes computational and memory overhead, allowing us to utilize the superior accuracy of UniQuan_F without extra deployment costs induced by the unification. Experimental results demonstrate that UniQuan_F outperforms existing UQ and BCQ methods, achieving up to 4.60% higher accuracy on GSM8K benchmark.