Researchers are testing LLMs with psychological benchmarks, chat-template probes, political dialogues and media framing tasks. The work uncovers context-dependent behavior and unintended effects of safety fine-tuning.
Safety fine-tuning is not neutral: it narrows the strategic space of LLMs in real debates. For on-premise deployments, control over weights, pipelines, and TCO becomes the real variable. AI-Radar's analysis focuses on the trade-off between ready-to-u...
A benchmark on political dialogues shows that LLMs prioritise logical defences and fail to exploit ethotic counterattacks in ad hominem exchanges. The study attributes this limit to safety fine-tuning, which narrows the strategic action space. For te...
A team built a 10,000-headline supervision set and classified 902,111 deduplicated headlines from 25 French outlets between 2022 and 2025. The framework separates rhetorical salience from editorial selection. Default thresholds inflate salience estim...
A study on eight open-source instruct models up to 9B parameters shows that the chat template acts as a switch: when present it raises disclaimers like "I'm just an AI" and lowers experiential expressions, and vice versa. Inside three models' activat...
PsyAgentBench reruns five classic psychological experiments on open-weight LLMs under named, blind, canonical, and counterfactual conditions. The results show that effects like Asch conformity depend on the prompt and safety filter, not on a stable b...
