研究者发现六种模式让AI文本如此疲惫阅读
纽约大学和德克萨斯大学在奥斯汀显示的研究前沿语言模型系统过度使用语言模式使得它们的输出更难阅读。
经常与ChatGPT,Claude或其他语言模型合作的任何人都知道这种感觉。 答案往往在事实上是正确的，但读起来却感觉自己像工作，充满了嵌套的句子，奇特的单词组合，比喻着人类永远不会使用。
纽约大学和德克萨斯大学奥斯丁分校的研究人员的研究以数据为后盾该团队有14个语言模型在闲聊中产生文本，编码会话，以及论证性散文。 阵列包括Claude,GPT-5.6,GLM-5,Kimi K2.5，以及Llama和Olmo等较小的开源型号。 研究人员总共分析了120次对话，45次编码会议，以及980篇散文，然后将它们与电影字幕，StackExchange，和学生散文中的人文文字进行比较。
使 AI 文本成为要读的字串的六种模式
AI模型过度使用罕见的单词组合，这些单词很少出现在人文写作中，如"阅读-patience预算"，它们将一个句子的相关部分放在了对工作记忆课税的方式上相去甚远。 兹Claude Opus 5生成了"一只总能起而走之的猫是一只更平静的猫"，其中主体和主上游被长条款隔开。
用掉的连接器缩短的引用也是常见的。 模型不是"教授所写的书"，而是写作"教授所写的书"，保存了一个字，却对各部分如何组合造成了短暂的混淆。 堆叠的名词如"恐怖电影音轨生成器"迫使读者自己想出哪个词会修改哪个词。
模型将名词变成人类不会使用的动词。 Claude Fable 5写了"日历它"，其中人类会说"把它放在日历中". 动词也被应用到无生命主体中。 兹Claude Opus 5制作了"整个对话都在吃自己的尾巴"，尽管对话没有尾巴，不能吃。 读者可以弄清楚其含义，但需要额外节拍。
更小的模型写得更像人类
Llama和奥尔莫基模最接近人类文本。 在奥尔莫家族内部，分数通过指示变体从基准模型稳步上升，到推理模型Olmo Think，达到前沿级别数字，指出后训练是可能的原因。 Claude Opus 5.5在21个比较中将它的前身Opus 5击败于19个比较，这表明有针对性的微调可以提高可读性。
培训奖励信息密度高于清晰度
人类语言平衡了制作的方便和易懂，但现代语言模型越来越多地接受数学，代码，逻辑推理等任务的培训，其中正确性得到奖赏，而清晰度不高，研究者们说。
从RLHF(从人类反馈中进行强制学习)到RLVR(用可验证的回报进行强制学习)的转变，将人类完全从反馈循环中去除，研究者争论，模型发展出一种"分辨"，对压缩信息和解决复杂问题很有效，但对人类读者来说却不好。
作为概念的证明，研究人员训练了一个较小的语言模型，其惩罚术语奖励较短的答案。 在某些培训条件下，该模式几乎无一例外地倾向于使用较短的措辞，不论背景如何，甚至普遍倾向于在培训期间从未遇到过的句子模式。 相比之下，人类则根据具体情况在较短的变体和更长的变体之间做出选择。
行业内部也有类似解释。 Anthropic员工杰克逊·克尼恩(Jackson Kernion)在Claude 微调上工作，他最近解释了为什么Opus 4.6是Anthropic最后一位优秀的"写作模型". 将Claude推向一种对AI很有效的风格，Kernion称其为"LLM心理学"的改编。 随着Opus 5.5的出现，Anthropic首次推后，解决了句子清晰度和超载的信息块，作为研究显示，成功实现。
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Researchers find six patterns that make AI text so exhausting to read
A study from New York University and the University of Texas at Austin shows that frontier language models systematically overuse linguistic patterns that make their output harder to read.
Anyone who regularly works with ChatGPT, Claude, or other language models knows the feeling. The answers are often factually correct, but reading them feels like work, full of nested sentences, odd word combinations, and metaphors no human would ever use.
A study by researchers at New York University and the University of Texas at Austin backs that up with data. The team had 14 language models produce text in casual conversation, coding sessions, and argumentative essays. The lineup included Claude, GPT-5.6, GLM-5, Kimi K2.5, and smaller open-source models like Llama and Olmo. In total, the researchers analyzed 120 conversations, 45 coding sessions, and 980 essays, then compared them against human-written text from movie subtitles, StackExchange, and student essays.
Six patterns that make AI text a chore to read
AI models overuse unusual word combinations that rarely show up in human writing, like "reader-patience budget," and they place related parts of a sentence far apart in ways that tax working memory. Claude Opus 5 generated "A cat who can always get up and away is a much calmer cat," where the subject and the main predicate are separated by a long clause.
Shortened references with a dropped connector are common too. Instead of "the book that the professor wrote," the models write "the book the professor wrote," saving one word but creating brief confusion about how the parts fit together. Stacked nouns like "horror movie soundtrack generator" force readers to figure out on their own which word modifies which.
The models turn nouns into verbs where humans wouldn't. Claude Fable 5 wrote "calendar it," where a human would say "put it in the calendar." They apply animate verbs to inanimate subjects, too. Claude Opus 5 produced "The whole conversation is eating its own tail," even though a conversation doesn't have a tail and can't eat. Readers can figure out what's meant, but it takes an extra beat.
Smaller models write more like humans
Llama and the Olmo base model come closest to human text. Within the Olmo family, scores rise steadily from the base model through the instruct variant to the reasoning model Olmo Think, which hits frontier-level numbers, pointing to post-training as the likely cause. Claude Opus 5.5 beats its predecessor Opus 5 in 19 out of 21 comparisons, suggesting that targeted finetuning can improve readability.
Training rewards information density over clarity
Human language balances ease of production with ease of understanding, but modern language models are trained increasingly on tasks like math, code, and logical reasoning, where correctness gets rewarded and clarity doesn't, the researchers say.
The shift from RLHF (Reinforcement Learning from Human Feedback) to RLVR (Reinforcement Learning with Verifiable Rewards) removes humans from the feedback loop entirely, the researchers argue, and models develop a kind of "dialect" that works well for compressing information and solving complex problems but poorly for human readers.
As a proof of concept, the researchers trained a smaller language model with a penalty term that rewarded shorter answers. Under certain training conditions, the model favored shorter phrasings almost without exception, regardless of context, and even generalized that preference to sentence patterns it had never encountered during training. Humans, by contrast, chose between shorter and longer variants depending on the situation.
A similar explanation comes from inside the industry. Anthropic employee Jackson Kernion, who works on Claude fine-tuning, recently explained why Opus 4.6 was the last good "writing model" from Anthropic. Optimizing newer models for math, code, and technical explanations aimed at other AI models pushed Claude toward a style that works well for AI but reads as too dense and unnatural for humans. Kernion called it an adaptation to "LLM psychology." With Opus 5.5, Anthropic pushed back for the first time, addressing sentence clarity and overloaded blocks of information, and as the study shows, successfully so.
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