aiqbench analysis
This research introduces two automated, curation-free metrics—the Lexical Alignment Score and Triangulated Preference Shift—to quantify lexical overuse and its link to human preference learning in LLMs. By validating these methods across six model families without manual intervention, the study offers a scalable framework for systematically diagnosing misalignment beyond scientific English. These tools provide a robust mechanism for evaluating the origins of divergence between model outputs and human expectations, directly aiding future alignment efforts.
Fully Automated Identification of Lexical Alignment and Preference-Stage Shifts in Large Language Models
arXiv:2606.03165v1 Announce Type: cross Abstract: The language used by digital chat assistants such as ChatGPT can diverge from human expectations (misalignment). Research, mostly on Scientific English, has described both what divergences occur and, to some extent, why, linking them to the training stage of human preference learning. Yet, existing approaches rely on manual curation. This paper introduces two curation-free, assumption-light evaluation metrics: the Lexical Alignment Score, which identifies lexical overuse, and the Triangulated Preference Shift, which quantifies how much of such shifts can be attributed to human preference learning. Using PubMed abstracts, continuations were generated and measured using windowed document prevalence across six model families (Falcon, Gemma, Llama, Mistral, OLMo, Yi). The procedure identifies, without manual intervention, overused items such as 'suggest', 'additionally', and 'strategy', and estimates their link to preference learning. Our findings replicate prior work and remain stable across parameter settings, random seeds, and evaluation on further data. The approach scales readily and enables systematic study of lexical (mis)alignment beyond Scientific English and across languages, and as such, the metrics have the potential to contribute to improved alignment for future models and understanding of its origins.
Abstract:The language used by digital chat assistants such as ChatGPT can diverge from human expectations (misalignment). Research, mostly on Scientific English, has described both what divergences occur and, to some extent, why, linking them to the training stage of human preference learning. Yet, existing approaches rely on manual curation. This paper introduces two curation-free, assumption-light evaluation metrics: the Lexical Alignment Score, which identifies lexical overuse, and the Triangulated Preference Shift, which quantifies how much of such shifts can be attributed to human preference learning. Using PubMed abstracts, continuations were generated and measured using windowed document prevalence across six model families (Falcon, Gemma, Llama, Mistral, OLMo, Yi). The procedure identifies, without manual intervention, overused items such as 'suggest', 'additionally', and 'strategy', and estimates their link to preference learning. Our findings replicate prior work and remain stable across parameter settings, random seeds, and evaluation on further data. The approach scales readily and enables systematic study of lexical (mis)alignment beyond Scientific English and across languages, and as such, the metrics have the potential to contribute to improved alignment for future models and understanding of its origins.
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
arXiv:2606.03165 [cs.CL]
(or arXiv:2606.03165v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.03165 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
https://doi.org/10.63317/4ut7ammh7z3h Focus to learn more DOI(s) linking to related resources
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Full text under the publisher's open licence · 291 words