How do you define the credibility of each of these articles? What measurements, what metrics does each of these articles make; what perspective does each offer research$-$wise? Paper $1$: Trustworthiness of Large Language Models $($LLMs$)$

Paper $2$: TrustLLM $[$arxiv$.$org$]$

Paper $3$: A Survey of Safety and Trustworthiness... $[$arxiv$.$org$]$

Paper $4$: On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? $[$arxiv$.$org$]$

Paper $5$: Towards Trustworthy Natural Language Generation through Dataset Curation $[$arxiv$.$org$]$

Paper $6$: Training Compute$-$Optimal Large Language ModelsarXiv preprint arXiv:$2203\mathrm{.}15556,2022.$

Paper $7$

DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Modelshttps:$//$doi$.$org$/10\mathrm{.}48550/$arXiv$\mathrm{.}2306\mathrm{.}11698$

Paper $8$ Evaluating the robustness of large language models. In Proceedings of the $35$th International Conference on Neural Information Processing Systems Nicholas Carlini et al $(2021)$

Paper $9$ Beyond Accuracy: Assessing the Trustworthiness of Large Language Models. arXiv preprint arXiv:$2303\mathrm{.}08366.$ Chen, A$.,$ et al$.(2023)$

Paper $10$ TrustScore: LLM Response Trustworthiness: an unreported assessment of the trustworthiness of LLM responses TrustScore: Reference$-$Free Evaluation of LLM Response Trustworthiness $($arxiv$.$org$)$