Papers by Leonardo Bertolazzi
LLMs instead of Human Judges? A Large Scale Empirical Study across 20 NLP Evaluation Tasks (2025.acl-short)
Copied to clipboard
Anna Bavaresco, Raffaella Bernardi, Leonardo Bertolazzi, Desmond Elliott, Raquel Fernández, Albert Gatt, Esam Ghaleb, Mario Giulianelli, Michael Hanna, Alexander Koller, Andre Martins, Philipp Mondorf, Vera Neplenbroek, Sandro Pezzelle, Barbara Plank, David Schlangen, Alessandro Suglia, Aditya K Surikuchi, Ece Takmaz, Alberto Testoni
| Challenge: | Existing evaluations of NLP models with LLMs are based on human judgments . however, there are concerns about their validity and reproducibility in proprietary models . |
| Approach: | They evaluate 11 current LLMs for their ability to replicate annotations. they show substantial variance across models and datasets. |
| Outcome: | The proposed model can replicate human annotations on 20 NLP datasets and show substantial variance across models and datasets. |
Teaching Small Language Models to Learn Logic through Meta-Learning (2026.eacl-long)
Copied to clipboard
| Challenge: | Large language models are increasingly evaluated on reasoning tasks, yet their logical abilities remain contested. |
| Approach: | They propose to apply few-shot meta-learning to large language models' reasoning domain to enable them to acquire abstract inference patterns that generalize to novel structures. |
| Outcome: | The proposed model outperforms GPT-4o and o3-mini on a syllogistic reasoning task. |
How Language Models Conflate Logical Validity with Plausibility: A Representational Analysis of Content Effects (2026.findings-acl)
Copied to clipboard
| Challenge: | a number of theories have been proposed to account for content effects in large language models, including the dual-process theory of reasoning, but the mechanisms behind content effects remain unclear. |
| Approach: | They propose to encode validity and plausibility concepts in LLMs by aligning them in representational geometry. |
| Outcome: | The proposed model conflates validity and plausibility, and vice versa. |
The Validation Gap: A Mechanistic Analysis of How Language Models Compute Arithmetic but Fail to Validate It (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies have explored methods to enhance self-correction in large language models, but little attention has been given to understanding the models’ internal mechanisms underlying error detection. |
| Approach: | They propose to use a large language model to analyze arithmetic errors in four smaller-sized LLMs and identify their internal mechanisms. |
| Outcome: | The proposed models heavily rely on consistency headstextemdashattention heads that assess surface-level alignment of numerical values in arithmetic solutions. |
A Systematic Analysis of Large Language Models as Soft Reasoners: The Case of Syllogistic Inferences (2024.emnlp-main)
Copied to clipboard
| Challenge: | syllogistic reasoning is a deductive reasoning skill that is crucial in everyday problem-solving and decision-making experiences. |
| Approach: | They propose to study the reasoning abilities of Large Language Models (LLMs) they propose to use supervised fine-tuning and chain-of-thought reasoning to investigate their results. |
| Outcome: | The proposed models exhibit reasoning biases, avoid answering that no conclusion follows, align with human difficulties, and struggle with multi-step reasoning. |