Papers by Filippo Pallucchini
SFAL: Semantic-Functional Alignment Scores for Distributional Evaluation of Auto-Interpretability in Sparse Autoencoders (2025.emnlp-industry)
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| Challenge: | Interpreting the internal representations of large language models (LLMs) is crucial for their deployment in real-world applications, impacting areas such as AI safety, debugging, and compliance. |
| Approach: | They propose an alternative evaluation strategy that assesses the alignment between the semantic neighbourhoods of features and their functional neighbourhoods by using co-occurrence statistics. |
| Outcome: | The proposed evaluation strategy reduces reliance on scoring on large-scale models and improves efficiency and cost-effectiveness. |
RE-FIN: Retrieval-based Enrichment for Financial data (2025.coling-industry)
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| Challenge: | Financial sentiment analysis (FSA) is a powerful tool to support business decision-making and perform financial forecasting. |
| Approach: | They propose a system that retrieves information from a knowledge base to enrich financial sentences, making them more knowledge-dense and explicit. |
| Outcome: | The proposed system generates propositions from the knowledge base and employs Retrieval-Augmented Generation (RAG) to augment the original text with relevant information. |
SAFE: A Sparse Autoencoder-Based Framework for Robust Query Enrichment and Hallucination Mitigation in LLMs (2025.findings-emnlp)
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| Challenge: | Large Language Models suffer from hallucinations, which can undermine their performance in critical applications. |
| Approach: | They propose a framework for detecting and mitigating hallucinations by leveraging SAEs. |
| Outcome: | The proposed framework improves query generation accuracy and mitigates hallucinations across datasets. |