Language can affect how people think. We ask a computational version: if a language model is trained on English that marks propositions as observed, reported, or inferred, does it use evidence differently in ordinary English? We create matched corpora in which particles either mark these routes (EVID), appear in the same positions without predicting source (CTRL), or are absent (NOMARK), and remove them at evaluation.

We test 405M-parameter Pythia models trained from scratch and Qwen3.5-4B Base and Post models adapted across three paired seeds. The models learn the mapping: Pythia reaches 92.8% before ordinary-English adaptation and retains 79.8% afterward; Qwen EVID averages about 80% for both Base and Post, with controls near chance. Visible markers shift Qwen’s scores. Once markers are removed, however, the Pythia tests cannot show whether the effect carries over. Base’s main Qwen result changes direction across seeds, and Post’s is negative in all three. A separate test suggests that Base may respond more to relevant context, but not specifically to source wording. No condition consistently improves reliability, multistep inference, or recognition of copied information. Source marking is easy to learn and can influence model scores, but we find no broader improvement in reasoning.

This paper presents novel methods to automatically convert posts and their comments from discussion forums such as Reddit into multi-turn dialogues. Our methods are generalizable to any forums; thus, they allow us to generate a massive amount of dialogues for diverse topics that can be used to pretrain language models. Four methods are introduced, Greedy_Baseline, Greedy_Advanced, Beam Search and Threading, which are applied to posts from 10 subreddits and assessed. Each method makes a noticeable improvement over its predecessor such that the best method shows an improvement of 36.3% over the baseline for appropriateness. Our best method is applied to posts from those 10 subreddits for the creation of a corpus comprising 10,098 dialogues (3.3M tokens), 570 of which are compared against dialogues in three other datasets, Blended Skill Talk, Daily Dialogue, and Topical Chat. Our dialogues are found to be more engaging but slightly less natural than the ones in the other datasets, while it costs a fraction of human labor and money to generate our corpus compared to the others. To the best of our knowledge, it is the first work to create a large multi-turn dialogue corpus from Reddit that can advance neural dialogue systems.