{
  "id": 19055,
  "url": "https://arxiv.org/abs/2608.11981v1",
  "title": "Benchmarking Trustworthiness of SLMs: Pre-trained vs. Compressed",
  "summary": "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",
  "authors": "Haokun Lin, Kaijie Zhu, Haobo Xu, Yichen Wu, Zhichao Lu, Qingfu Zhang, Zhenan Sun",
  "category": "research",
  "topics": null,
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-12T12:14:02.000Z",
  "fetched_at": "2026-08-13T05:10:37.786Z",
  "source_slug": "x-arxiv-cs-cl-ethics-relevant-nlp",
  "source_name": "arXiv cs.CL (ethics-relevant NLP)",
  "source_homepage": "https://arxiv.org/list/cs.CL/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/19055",
  "original_url": "https://arxiv.org/abs/2608.11981v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}