Results show that explanation form does not affect decision accuracy but significantly reshapes reliance: fluent natural language explanations heighten moderators’ over-reliance on the AI—leading them to follow it even when it errs—whereas feature-based explanations promote scrutiny and self-reliance.
Bing Tuo, Tim Miller, Gianluca Demartini· Proceedings of the 2026 ACM...· 0 citations
It is found that the conceptual target of a definition drives annotation divergence, while construct-preserving elaboration does not: conceptual framing significantly shifts annotations for humans and does so even more strongly for LLMs.
Martin Wessel, Timo Spinde, Jürgen Pfeffer et al.· 0 citations
These findings position persona-conditioned judging as a controlled sensitivity probe for stress-testing LLM-based IR evaluation pipelines and identifying systems whose evaluation outcomes are sensitive to assessor framing.
Samaneh Mohtadi, Pietro Bernardelle, Joel Mackenzie et al.· 0 citations
Large language models (LLMs) are increasingly deployed in politically sensitive contexts, raising concerns about their susceptibility to ideological biases. In this work, we examine how synthetic persona conditioning shapes ideological expression across seven open-weight instruction-tuned models (7B-72B parameters) usi...
Pietro Bernardelle, Stefano Civelli, Leon Fröhling et al.· Information Processing &...· 5 citations
This work studies whether query-document (q-d) relevance is linearly decodable from residual-stream activations in instruction-tuned LLMs, how this signal compares with generated relevance judgments, and whether it transfers across languages.
Pietro Bernardelle, Samaneh Mohtadi, Stefano Civelli et al.· 0 citations
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