Papers by Giovanni Trappolini
Redefining Retrieval Evaluation in the Era of LLMs (2026.eacl-long)
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| Challenge: | Traditional IR metrics assume that humans examine documents sequentially with diminishing attention to lower ranks. |
| Approach: | They propose a utility-based annotation schema that quantifies positive contribution of relevant passages and negative impact of distracting ones. |
| Outcome: | The proposed metric improves correlation with the end-to-end answer accuracy by up to 36% compared to traditional metrics. |
DanteLLM: Let’s Push Italian LLM Research Forward! (2024.lrec-main)
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| Challenge: | Existing models for large language processing in the English language are limited in resources and evaluation tools for non-English languages. |
| Approach: | They propose a benchmark and an open LLM Leaderboard to evaluate LLMs’ performance in Italian and propose 'DanteLLM' it is the most performant LLM in the world, with improvements of up to 6 points . |
| Outcome: | The proposed model outperforms existing models in Italian and offers a blueprint for the development and evaluation of LLMs in other languages. |
The Mechanics of Interference: Defusing Distractors in RAG via Sparse Autoencoder Interventions (2026.findings-acl)
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| Challenge: | Large language models exhibit a critical vulnerability to distractor interference when processing retrieval-augmented contexts. |
| Approach: | They propose a mechanistic framework that corrects this failure mode through targeted interventions in the model’s latent space. |
| Outcome: | The proposed framework achieves recovery rates of up to 94% on distractor-vulnerable samples on Gemma-2 and Llama-3 model families across three QA benchmarks. |