Papers by Dario Onorati
KERMIT: Complementing Transformer Architectures with Encoders of Explicit Syntactic Interpretations (2020.emnlp-main)
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Fabio Massimo Zanzotto, Andrea Santilli, Leonardo Ranaldi, Dario Onorati, Pierfrancesco Tommasino, Francesca Fallucchi
| Challenge: | Syntactic parsers are losing their centrality in downstream tasks due to the success of large-scale textual representation learners. |
| Approach: | They propose to embed symbolic syntactic parse trees into artificial neural networks to visualize how syntax is used in inference. |
| Outcome: | The proposed encoder can visualize how syntax is used in inference. |
Measuring bias in Instruction-Following models with P-AT (2023.findings-emnlp)
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| Challenge: | Instruction-Following Language Models (IFLMs) are promising and versatile tools for solving many downstream, information-seeking tasks. |
| Approach: | They propose a resource to test whether IFLMs are prone to biases . they cast WEAT word tests in promptized classification tasks and associate a metric - the bias score . |
| Outcome: | The proposed resource consists of 2310 prompts and tests gender and race biases in all the analyzed models. |
Investigating the Impact of Data Contamination of Large Language Models in Text-to-SQL translation (2024.findings-acl)
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Federico Ranaldi, Elena Sofia Ruzzetti, Dario Onorati, Leonardo Ranaldi, Cristina Giannone, Andrea Favalli, Raniero Romagnoli, Fabio Massimo Zanzotto
| Challenge: | Large Language Models (LLMs) understand textual description to generate code in zero-shot scenarios, but there is a possibility that this ability may be influenced by having seen target textual descriptions and the related code. |
| Approach: | They propose a method to detect Data Contamination in Large Language Models (LLMs) and analyze their results on Termite and Spider Datasets to investigate their method. |
| Outcome: | The proposed method detects data contamination in GPTs and analyzes its performance on unfamiliar datasets. |
A Trip Towards Fairness: Bias and De-Biasing in Large Language Models (2024.starsem-1)
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| Challenge: | a little or a large bias in CtB-LLMs may cause huge harm . LLaMA and OPT families have an important bias in gender, race, religion, and profession. |
| Approach: | They propose to debiase three families of Very Large-Language Models with LORA to reduce bias by 4.12 points in the normalized stereotype score. |
| Outcome: | The proposed model reduces bias up to 4.12 points in the normalized stereotype score. |