Papers by Dario Di Palma

2 papers
LLaMAs Have Feelings Too: Unveiling Sentiment and Emotion Representations in LLaMA Models Through Probing (2025.acl-long)

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Challenge: Large Language Models (LLMs) have become central to NLP, demonstrating their ability to adapt to various tasks through prompting techniques.
Approach: They probe the hidden layers of Large Language Models to identify where sentiment features are most represented and to assess how this affects sentiment analysis.
Outcome: The proposed approach enables sentiment tasks to be performed with memory requirements reduced by an average of 57%.
Are the Hidden States Hiding Something? Testing the Limits of Factuality-Encoding Capabilities in LLMs (2025.acl-long)

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Challenge: Recent studies suggest that LLMs encode internal representations of factuality when generating inaccurate or fabricated content.
Approach: They propose a strategy for sampling plausible true-false factoid sentences from tabular data and a procedure for generating realistic, LLM-dependent true-False datasets from Question Answering collections.
Outcome: The proposed approach lays the groundwork for future research on factuality in LLMs and offers practical guidelines for more effective evaluation.

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