Papers by Eliana Pastor
Detecting and Mitigating Challenges in Zero-Shot Video Summarization with Video LLMs (2025.findings-acl)
Copied to clipboard
Luca Cagliero, Lorenzo Vaiani, Eliana Pastor, Alkis Koudounas, Elena Baralis, Vittorio Mazzia, Sandro Pollastrini, Thomas Gueudre, Manuel Giollo, Daniele Amberti, Yue Wu
| Challenge: | Video Large Language Models (VLLMs) exhibit impressive zero-shot capabilities in video analysis, but their performance varies significantly depending on the LLM prompt, the characteristics of the video, and the properties of the training data and LLM architecture. |
| Approach: | They propose to use Chain-of-Thought prompting to inject knowledge extracted by external, lightweight models into video summarization benchmarks to evaluate their performance. |
| Outcome: | The proposed solutions improve summarization performance by injecting knowledge extracted by external, lightweight models. |
ferret: a Framework for Benchmarking Explainers on Transformers (2023.eacl-demo)
Copied to clipboard
| Challenge: | Existing methods for interpreting transformer outputs are scattered and hard to operationalize. |
| Approach: | They propose a Python library to simplify the use and comparisons of XAI methods on transformers. |
| Outcome: | The proposed method provides better explanations and is preferable in the context of transformer models. |
Explaining Speech Classification Models via Word-Level Audio Segments and Paralinguistic Features (2024.eacl-long)
Copied to clipboard
| Challenge: | Existing explanations for speech classification models are difficult to interpret and make mistakes. |
| Approach: | They propose to explain speech classification models by using word-level and paralinguistic attributes to measure the impact of each audio segment aligned with a word on the outcome. |
| Outcome: | The proposed explanations correctly represent the model’s inner workings and are plausible to humans. |
Privacy Preserving Data Selection for Bias Mitigation in Speech Models (2025.acl-industry)
Copied to clipboard
Alkis Koudounas, Eliana Pastor, Vittorio Mazzia, Manuel Giollo, Thomas Gueudre, Elisa Reale, Luca Cagliero, Sandro Cumani, Luca De Alfaro, Elena Baralis, Daniele Amberti
| Challenge: | Existing methods for identifying subgroups raise privacy concerns and gather sensitive information at runtime might be impractical. |
| Approach: | They propose a method to identify and train underperforming subgroups and train a model to predict if an utterance belongs to these subgroup. |
| Outcome: | The proposed method reduces biases and improves performance on intent classification and automatic speech recognition tasks. |