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A Red Teaming Framework for Large Language Models: A Case Study on Faithfulness Evaluation

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arXiv:2606. 25476v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated remarkable performance across natural language processing tasks, yet their deployment in high-stakes applications raises critical concerns regarding reliability, safety, and trustworthiness.

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Authors: Abrar Alotaibi, Raed Mughus, Moataz Ahmed
Released: 2026-06-25 (14d ago)
Topic: Related to current project
Relation to my work: Read next because A Red Teaming Framework for Large Language Models: A Case Study on Faithfulness Evaluation overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: text, eval, rate, full, contexts, language, model. Source: arxiv cs.CL (NLP).
arXiv: 2606.25476

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