Large language models (LLMs) are entering decisions in triage and lending, where task-relevant inference must be distinguished from impermissible proxy use. Current audits ask whether decisions change...
arXiv:2606. 29034v1 Announce Type: cross Abstract: Large language models (LLMs) increasingly summarize clinical evidence, where a claim's weight depends on how strongly it is supported.
arXiv:2607. 05355v1 Announce Type: cross Abstract: Attribution scores increasingly identify which neuron rows of a language model matter for applications such as pruning, interpretability, and editing for safety, yet whether they identify causally important rows is rarely tested directly.
arXiv:2606. 17165v1 Announce Type: cross Abstract: Organizations and researchers show increasing interest in using large language models (LLMs) in place of human participants in A/B tests, in the hope of experimenting faster and at lower cost.
The paper investigates whether language models still encode occupational biases even when they appear unbiased in behavioral tests. Using a causal framework, the authors separate bias into internal representations of user competence and observable outputs, deriving steering vectors that show these representations influence model behavior in question‑answering and hiring tasks. Across several open‑weight models, demographic factors such as gender, race, and socioeconomic status affect the models’ internal competence representations, revealing hidden bias that behavioral metrics alone may miss.
arXiv:2608. 14320v1 Announce Type: new Abstract: The anchoring effect is a cognitive bias in which an initial reference value shifts a later judgment toward itself.
arXiv:2606. 27383v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as research assistants, yet it remains unclear whether they can calibrate research takeaways to the strength and scope of the supporting evidence.
arXiv:2607. 19355v1 Announce Type: new Abstract: LLMs are increasingly used with external knowledge sources like the internet.
arXiv:2603. 05308v3 Announce Type: replace-cross Abstract: Assessing whether an article supports an assertion is essential for hallucination detection and claim verification.
arXiv:2605.01048v2 Announce Type: replace-cross Abstract: Counterfactual prompting (i.e., perturbing a single factor and measuring output change) is widely used to evaluate things like LLM bias and C...
arXiv:2603. 13891v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used for automated text annotation in tasks ranging from academic research to content moderation and hiring.
The paper investigates how the inference setup of large language models (LLMs) influences their behavior in a medical resource‑allocation scenario. By comparing paired‑context and independent‑inference experiments, the authors show that adding a single contrasting patient sentence can shift the model’s probability assignments in opposite directions across most tested models. Additional experiments varying scenario attributes further demonstrate that patient information can have context‑dependent effects on LLM outputs.