LLMs excel in natural language understanding but are resource-intensive, limiting their accessibility. Smaller models like MiniCPM offer better scalability but often need targeted optimization to perform. Text embeddings, vector representations that capture semantic information, are essential for tasks like document classification and information retrieval. While LLMs such as GPT-4, LLaMA, and Mistral achieve strong performance due to extensive training, smaller models like Gemma, Phi, and MiniCPM require specific optimizations to close the performance gap and remain efficient. Tsinghua University’s researchers investigated ways to enhance smaller language models by improving their text embeddings. They focused on three models—MiniCPM, Phi-2, and Gemma—and applied contrastive fine-tuning using the NLI dataset. The findings revealed that this method significantly improved text embedding quality across various benchmarks, with MiniCPM showing a notable 56.33% performance gain. […]
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