[Submitted on 23 Sep 2026]
Title:Technical Manual for Toolkit for Confidence-Corpus Consistency via Fine-Tuning on a Fabricated Corpus
View a PDF of the paper titled Technical Manual for Toolkit for Confidence-Corpus Consistency via Fine-Tuning on a Fabricated Corpus, by Jos\'e Luciano Ver\c{c}osa Marques and 4 other authors
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Abstract:A language model's confidence in an answer is often read as a proxy for how well it knows the corresponding fact. This manual documents an open toolkit built to test that reading directly: a small causal language model is fine-tuned on a corpus that consistently asserts one fabricated arithmetic answer for each of the 81 single-digit addition pairs, and its post-fine-tuning confidence in each fabricated answer is compared against its own pre-fine-tuning confidence in the corresponding true answer, using an unchanged measurement procedure throughout. We describe and justify every pipeline stage, fact-space generation, token-length-aware confidence measurement, baseline validation, corpus construction, fine-tuning, and paired before/after comparison, together with the confound each is meant to rule out, among them tokenization asymmetry between single- and double-digit answers and the difference between an answer merely losing its edge and one being actively suppressed. This manuscript is a methodological and implementation reference: it documents the instrument and does not report or interpret the outcome of any specific run. The toolkit and its pinned dependency environment are archived separately (Section 9) under a persistent identifier, to be cited as an instrument by work that produces and interprets empirical results with it.
Comments: 30 pages, 2 figures, 1 table, 12 code listings. Methodological and implementation reference manual; does not report or interpret empirical results from any specific run. Toolkit and pinned dependency environment archived at this https URL (CC BY 4.0)
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
MSC classes: 68T50, 68T07
ACM classes: I.2.6; I.2.7
Cite as: arXiv:2609.28747 [cs.CL]
(or arXiv:2609.28747v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.28747
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: José Luciano Verçosa Marques [view email] [v1] Wed, 23 Sep 2026 19:43:59 UTC (102 KB)
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