Benchmarking Trustworthiness of SLMs: Pre-trained vs. Compressed

Small Language Models (SLMs) have emerged as a more efficient alternative to traditional Large Language Models (LLMs), offering promising potential in resource-constrained scenarios. Existing approaches to building SLMs typically follow two paths: training compact models from scratch, or compressing larger pre-trained models using methods such as pruning, quantization, or distillation. As language models become increasingly integrated into real-world applications, ensuring their trustworthiness

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ethics.ai (12 August 2026), “Benchmarking Trustworthiness of SLMs: Pre-trained vs. Compressed,” evidence record 19055, https://ethics.ai/record/19055 (originally published by arXiv cs.CL (ethics-relevant NLP)).

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