# Post-training effects on LLM vocabulary > Source-anchored research for Post-training effects on LLM vocabulary. 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/) · 4 facets · 7 facts · page: https://llms-explorer.com/tree/post-training-effects-on-llm-vocabulary/ ## Measurements and reference values - The popular explanation that 'delve' is overused because OpenAI's outsourced RLHF annotators in Nigeria favor Nigerian formal English was proposed by Alex Hern and is framed as a plausible theory; circumstantial support is PubMed 'delve' usage rising 10-100x, with no direct causal evidence. (medium confidence) — [source](https://simonwillison.net/2024/Apr/18/delve/) ## Problems, failure modes and limitations - Current lexical-shift studies cannot test ChatGPT directly because it lacks a public base variant, and they use mid-size open models on English scientific text, limiting generalization to frontier chat assistants. (low confidence) — [source](https://arxiv.org/html/2606.03165) ## Comparisons and alternatives - Top overused lemmas in instruction-tuned models versus human abstracts include suggest, highlight, enhance, furthermore, additionally, crucial, potential, strategy; many are ordinary scientific vocabulary, so models amplify already-typical wording rather than inventing novel words. (low confidence) — [source](https://arxiv.org/html/2606.03165) - Base models also diverge from human word usage (misalignment scores 0.0329-0.0345 vs human baseline 0.0304), so not all lexical drift is post-training; content-word alignment trends are weakened or reversed after instruction tuning. (low confidence) — [source](https://arxiv.org/html/2606.03165) ## Facts and statements - A 2026 study of 42,000 PubMed abstract continuations across six open model families (Falcon, Gemma, Llama, Mistral, OLMo, Yi; 4B-8B) defines curation-free metrics (Lexical Alignment Score, Triangulated Preference Shift) to isolate overuse relative to human text and shifts attributable to preference-learning stages. (medium confidence) — [source](https://arxiv.org/html/2606.03165) - Instruction tuning does not uniformly change lexical diversity: in Qwen2.5-7B, Mistral-7B and Llama-3.1-8B on three multiple-choice benchmarks, cross-rationale similarity diversity (1-SelfBLEU) consistently fell while surface Unique-2 diversity moved in both directions, so 'post-training narrows vocabulary' is model- and metric-dependent. (medium confidence) — [source](https://arxiv.org/html/2608.13430) - Markdown-heavy training data leaves stylistic fingerprints such as em-dash frequency; post-training can modify but does not fully erase these markers. (low confidence) — [source](https://arxiv.org/pdf/2603.27006)