Measuring behavioral signals of LLM through psychometric profiling
We develop a cross-linguistic psychometric profiling framework and evaluate nine LLMs using seven psychological instruments, with five repeated administrations per model and language in Chinese and English. Items unresolved after a prespecified retry procedure are retained as NA. Joint analysis of scored and NA responses captures response tendencies and boundaries of self-report applicability.
- ▪We develop a cross-linguistic psychometric profiling framework and evaluate nine LLMs using seven psychological instruments, with five repeated administrations per model and language in Chinese and English.
- ▪Items unresolved after a prespecified retry procedure are retained as NA.
- ▪Joint analysis of scored and NA responses captures response tendencies and boundaries of self-report applicability.
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| Original publisher | arXiv.org |
| Canonical URL | https://arxiv.org/abs/2609.22934 |
| Publication time | Wed, 23 Sep 2026 02:46:17 +0000 |
| Retrieval time | 2026-09-23T02:49:30.388Z |
| Last seen | 2026-09-23T02:49:30.388Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | ZO2xed8vdMlk · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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Computer Science > Computation and Language arXiv:2609.22934 (cs) [Submitted on 19 Sep 2026] Title:Measuring Behavioural Signatures of Large Language Models through Psychometric Profiling Authors:Yu Sha, Junqi Tao, Dixin Zhou, Yansheng Tu, Mingyang Chen, Xiang Fan, Yang Liu, Mengquan Yang, Jie Lin, Jiahui Fu, Hua Zheng, Benwei Zhang, Zhou Kai View a PDF of the paper titled Measuring Behavioural Signatures of Large Language Models through Psychometric Profiling, by Yu Sha and 12 other authors View PDF HTML (experimental) Abstract:Large language models (LLMs) increasingly mediate human decisions and communication, yet their behavioural regularities remain difficult to characterize systematically.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv.org.
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