Same Judgment, Different Yes: Measuring Multilingual Judgment Profiles in Language Models
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
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Large language models (LLMs) increasingly issue judgments read as binary verdicts, and a growing literature reports such judgments shifting under logically irrelevant changes of wording - among them an amplified yes-no bias on moral dilemmas, absent in humans. A single framing cannot say what such a shift is: in a yes/no question the word "no" is at once logical verdict, lexical token, and last-printed option.
arXiv:2607. 05552v1 Announce Type: cross Abstract: Large language models (LLMs) increasingly issue judgments read as binary verdicts, and a growing literature reports such judgments shifting under logically irrelevant changes of wording - among them an amplified yes-no bias on moral dilemmas, absent in humans.
arXiv:2610.32144v1 Announce Type: new Abstract: When a language model is asked whether its own answer is correct, three languages are involved: that of the question, that requested for the answer, an...
The paper investigates why large language models (LLMs) accept or reject generic statements, drawing on psycholinguistic research that identifies factors beyond mere property frequency, such as distinctiveness and characteristicness. Using twelve LLMs evaluated on a psycholinguistic dataset and real-world generics, the study finds that models weigh property commonality against distinctiveness similarly to humans, but they prioritize factual truth and largely ignore striking properties. Manipulating hidden states along factor directions confirms that each factor influences judgments as predicted.
Psycholinguistic studies explore why humans accept some generic statements but reject others by showing that these judgments depend on more than how common a property is. People consider several other...
The study investigates how different prompting strategies affect large language model (LLM) judges across multiple languages and tasks. Using a benchmark of 9 prompting strategies, 4 open-weight judges, 27 languages, and 4 tasks, the authors collected over 1 million pointwise scores validated against human judgments. Results show that prompting effectiveness is highly dependent on the judge model, can reverse direction across judges, and becomes less stable in low‑resource languages, indicating that prompt choice must be tailored to the specific evaluation setting.