# Pretraining versus alignment attribution of lexical tics > Source-anchored research for Pretraining versus alignment attribution of lexical tics. Run status: COMPLETED. Parent: [Why LLMs converge on the same words: mode collapse and post-training lexical effects](https://llms-explorer.com/tree/ai-isms-origins/) · 2 facets · 5 facts · page: https://llms-explorer.com/tree/pretraining-versus-alignment-attribution-of-lexical-tics/ ## How it works - Follow-up work reports that alignment training (RLHF, DPO) substantially increases focal-word overuse relative to base models, so pretraining alone is not the sole cause. Both papers share the same authors, so this is not independent replication. (medium confidence) — [source](https://arxiv.org/pdf/2412.11385) ## Facts and statements - Juzek and Ward emulated learning from human feedback on Llama and found participants systematically preferred text variants containing focal words, consistent with human-feedback training contributing to lexical overuse. (medium confidence) — [source](https://arxiv.org/pdf/2412.11385) - The Nigerian-annotator theory (outsourced RLHF raters favour Nigerian formal English, hence 'delve') was circulated as speculation, called 'a pretty solid theory' by Willison, without a causal test. (medium confidence) — [source](https://simonwillison.net/2024/Apr/18/delve/) - Juzek and Ward state that evaluators appeared to treat 'delve' differently from other focal words, so human preference does not cleanly explain that word, and factors beyond RLHF and pretraining data contribute. (low confidence) — [source](https://arxiv.org/pdf/2412.11385) - A 2026 preprint argues markdown-heavy pretraining text leaves fingerprints such as em dashes, and reports em-dash rates differing by post-training recipe (GPT-4.1 ~10.6 per 1,000 words, Claude Opus ~9.1, Llama ~0), implying both stages shape the tic. Rates come from a search snippet, not verified in the full text. (low confidence) (verified as of 2026-09-23) — [source](https://arxiv.org/pdf/2603.27006)