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Raquel Fern{\'a}ndez

5 accepted papers

2025

Playpen: An Environment for Exploring Learning From Dialogue Game Feedback

Nicola Horst, Davide Mazzaccara, Antonia Schmidt, Michael Sullivan, Filippo Moment{\`e}, Luca Franceschetti +10
EMNLP 2025

Interaction between learner and feedback-giver has come into focus recently for post-training of Large Language Models (LLMs), through the use of reward models that judge the appropriateness of a model’s response. In this paper, we investigate whether Dialogue Games—goal-directed and rule-governed a

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2025

RAcQUEt: Unveiling the Dangers of Overlooked Referential Ambiguity in Visual LLMs

Alberto Testoni, Barbara Plank, Raquel Fern{\'a}ndez
EMNLP 2025

Ambiguity resolution is key to effective communication. While humans effortlessly address ambiguity through conversational grounding strategies, the extent to which current language models can emulate these strategies remains unclear. In this work, we examine referential ambiguity in image-based que

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2025

Reading Between the Prompts: How Stereotypes Shape LLM’s Implicit Personalization

Vera Neplenbroek, Arianna Bisazza, Raquel Fern{\'a}ndez
EMNLP 2025

Generative Large Language Models (LLMs) infer user’s demographic information from subtle cues in the conversation — a phenomenon called implicit personalization. Prior work has shown that such inferences can lead to lower quality responses for users assumed to be from minority groups, even when no d

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2025

Triangulating LLM Progress through Benchmarks, Games, and Cognitive Tests

Filippo Moment{\`e}, Alessandro Suglia, Mario Giulianelli, Ambra Ferrari, Alexander Koller, Oliver Lemon +3
EMNLP 2025

We examine three evaluation paradigms: standard benchmarks (e.g., MMLU and BBH), interactive games (e.g., Signalling Games or Taboo), and cognitive tests (e.g., for working memory or theory of mind). First, we investigate which of the former two—benchmarks or games—is most effective at discriminatin

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2025

When Models Reason in Your Language: Controlling Thinking Language Comes at the Cost of Accuracy

Jirui Qi, Shan Chen, Zidi Xiong, Raquel Fern{\'a}ndez, Danielle Bitterman, Arianna Bisazza
EMNLP 2025

Recent Large Reasoning Models (LRMs) with thinking traces have shown strong performance on English reasoning tasks. However, the extent to which LRMs can think in other languages is less studied. This is as important as answer accuracy for real-world applications since users may find the thinking tr

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