activity
20232026
most citedHave Faith in Faithfulness: Going Beyond Circuit Overlap When Finding Model Mechanisms

2 citations · 3 across the 9 of their papers we have counts for

collaborators

9 papers

cs.CL2026

Where Animacy Lives in Large Language Models: Tracing the Circuits of the Animacy Concept

Samuele Punzo, Giovanni Cinà, Sandro Pezzelle

Distinguishing animate from inanimate concepts in written language requires more than shallow text processing, as it involves recognizing complex selectional constraints and contex…

cs.CL2026

Is my model perplexed for the right reason? Contrasting LLMs' Benchmark Behavior with Token-Level Perplexity

Zoë Prins, Samuele Punzo, Frank Wildenburg +2

Standard evaluations of Large language models (LLMs) focus on task performance, offering limited insight into whether correct behavior reflects appropriate underlying mechanisms an…

cs.CL2026

Who is the richest club in the championship? Detecting and Rewriting Underspecified Questions Improve QA Performance

Yunchong Huang, Gianni Barlacchi, Sandro Pezzelle

Large language models (LLMs) perform well on well-posed questions, yet standard question-answering (QA) benchmarks remain far from solved. We argue that this gap is partly due to u…

cs.CL2026

Beyond Divergent Creativity: A Human-Based Evaluation of Creativity in Large Language Models

Kumiko Nakajima, Jan Zuiderveld, Sandro Pezzelle

Large language models (LLMs) are increasingly used in verbal creative tasks. However, previous assessments of the creative capabilities of LLMs remain weakly grounded in human crea…

cs.CL2025

They want to pretend not to understand: The Limits of Current LLMs in Interpreting Implicit Content of Political Discourse

Walter Paci, Alessandro Panunzi, Sandro Pezzelle

Implicit content plays a crucial role in political discourse, where speakers systematically employ pragmatic strategies such as implicatures and presuppositions to influence their…

cs.CL2025

Are formal and functional linguistic mechanisms dissociated in language models?

Michael Hanna, Yonatan Belinkov, Sandro Pezzelle

Although large language models (LLMs) are increasingly capable, these capabilities are unevenly distributed: they excel at formal linguistic tasks, such as producing fluent, gramma…

cs.LG2024★ 2 cited

Have Faith in Faithfulness: Going Beyond Circuit Overlap When Finding Model Mechanisms

Michael Hanna, Sandro Pezzelle, Yonatan Belinkov

Many recent language model (LM) interpretability studies have adopted the circuits framework, which aims to find the minimal computational subgraph, or circuit, that explains LM be…

cs.CL2024

Do Pre-Trained Language Models Detect and Understand Semantic Underspecification? Ask the DUST!

Frank Wildenburg, Michael Hanna, Sandro Pezzelle

In everyday language use, speakers frequently utter and interpret sentences that are semantically underspecified, namely, whose content is insufficient to fully convey their messag…

cs.CL2023★ 1 cited

When Language Models Fall in Love: Animacy Processing in Transformer Language Models

Michael Hanna, Yonatan Belinkov, Sandro Pezzelle

Animacy - whether an entity is alive and sentient - is fundamental to cognitive processing, impacting areas such as memory, vision, and language. However, animacy is not always exp…