aiqbench analysis
This research introduces a novel framework for evaluating Large Language Models based on register-specific linguistic patterns rather than just factual accuracy or task performance. By utilizing Maximum Mean Discrepancy against human baselines, the study reveals that model proximity to human language varies by context and is not strictly determined by model size. This work advances the understanding of LLM limitations in capturing nuanced communicative contexts essential for natural human interaction.
How Human-Like Are Large Language Models? A Register-Aware Linguistic Evaluation Framework
arXiv:2605.23651v3 Announce Type: replace Abstract: While factual correctness and task-performance have been in focus of Large Language Model (LLM) research for a long time, the fundamental question of how human-like generated texts are on a linguistic level has been underexplored. From a corpus-linguistic perspective, language production is inherently context-dependent, with distinct communicative contexts giving rise to differences in frequencies and co-occurrence patterns of linguistic features. A text failing to adhere to these patterns can be content-wise correct, but still be unfavorable to human readers. In this work, we propose a context-aware evaluation framework in which human-likeness is assessed using a two-sample problem between the linguistic feature distribution of a human reference corpus for a given register and a corresponding LLM-generated corpus. We implement this framework using the Maximum Mean Discrepancy (MMD) and the 67 lexico-grammatical features introduced by Biber, which are commonly applied in corpus linguistics. In our experiments, we compare seven instruction-tuned, open-source models across five English-language datasets spanning distinct registers against a human baseline. While across all tested setups, LLMs deviate from the human baseline, which models are closest to human language depends on the register and is not dictated by model size.
Title:How Human-Like Are Large Language Models? A Register-Aware Linguistic Evaluation Framework
Abstract:While factual correctness and task-performance have been in focus of Large Language Model (LLM) research for a long time, the fundamental question of how human-like generated texts are on a linguistic level has been underexplored. From a corpus-linguistic perspective, language production is inherently context-dependent, with distinct communicative contexts giving rise to differences in frequencies and co-occurrence patterns of linguistic features. A text failing to adhere to these patterns can be content-wise correct, but still be unfavorable to human readers. In this work, we propose a context-aware evaluation framework in which human-likeness is assessed using a two-sample problem between the linguistic feature distribution of a human reference corpus for a given register and a corresponding LLM-generated corpus. We implement this framework using the Maximum Mean Discrepancy (MMD) and the 67 lexico-grammatical features introduced by Biber, which are commonly applied in corpus linguistics. In our experiments, we compare seven instruction-tuned, open-source models across five English-language datasets spanning distinct registers against a human baseline. While across all tested setups, LLMs deviate from the human baseline, which models are closest to human language depends on the register and is not dictated by model size.
9 pages (main) + 31 pages appendix, 29 figures, 10 tables. Code and data: this https URL
Computation and Language (cs.CL)
arXiv:2605.23651 [cs.CL]
(or arXiv:2605.23651v3 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2605.23651 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 311 words
