The paper investigates why AI-generated text has become harder to understand, focusing on psycholinguistic features linked to human processing difficulty such as object relative clauses, long syntactic dependencies, and low-frequency n-grams. Using a new corpus of AI-generated text from diverse domains, the study finds that frontier and near-frontier language models employ these difficult features more frequently than paraphrases from round-trip translation and human writing, including more inanimate subjects with animate verbs, longer dependencies, stacked nouns, and reduced object relatives. The authors hypothesize that AI models prioritize information density for tasks like math, code, and reasoning, diverging from human communication goals, and demonstrate this by fine‑tuning a model with a length penalty, which reduces human‑like linguistic behavior.
By Thomas Hikaru Clark, Matias B. Martin, Kyle Mahowald
The paper investigates whether language models possess latent rhetorical abilities, specifically analogy reasoning, before instruction tuning. By extracting an "Analogy Vector" from the residual stream, the authors demonstrate that this vector can steer explanation style across several instruction-tuned models, improving performance metrics. Their findings reveal a continuous gradient of behavioral accessibility tied to the amount of instruction exposure during training, challenging the binary base‑vs‑instruct distinction and suggesting that instruction exposure shapes rhetorical knowledge in activation space.
By Shengjie Xu
arXiv:2608. 14681v1 Announce Type: cross Abstract: Words recur constantly in natural language use, yet it remains unclear whether language models reactivate prior representations or re-evaluate repeated words afresh, and whether post-training changes this default behavior.
By Jinglei Ren, Yuyue Wang
The study evaluates eleven autoregressive transformer models on English agreement attraction scenarios using a surprisal-based approach. Results show that while transformers match human reading times for prepositional phrase configurations, they perform poorly on object‑extracted relative clauses, with predictions diverging across models and failing to capture human interference patterns. The authors argue that current transformers cannot adequately model human morphosyntactic processing and call for more rigorous, comprehensive testing to avoid misleading conclusions from limited syntactic setups.
By Titus von der Malsburg, Sebastian Pad\'o
The paper studies how large language models (LLMs) handle the discourse particles "just" and "only" in natural conversation. Behavioral experiments show that both humans and LLMs prefer these particles and are more sensitive to "just" than "only", while instruction tuning enhances this sensitivity. Mechanistic analysis identifies specific units in the models that respond to each particle, revealing an asymmetric overlap in their internal representations.
By William Sheffield, Jiahui Chen, Amy Zhang, Alex Warstadt, Junyi Jessy Li, Kanishka Misra
arXiv:2604. 19139v3 Announce Type: replace-cross Abstract: As Large Language Models (LLMs) continue to evolve through alignment techniques such as Reinforcement Learning from Human Feedback (RLHF) and Constitutional AI, a growing and increasingly conspicuous phenomenon has emerged: the proliferation of verbal tics--repetitive, formulaic linguistic patterns that pervade model outputs.
By Shuai Wu, Xue Li, Yanna Feng, Yufang Li, Zhijun Wang, Ran Wang