Papers by Luca Benedetto
Beyond Accuracy Optimization: Computer Vision Losses for Large Language Model Fine-Tuning (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have demonstrated impressive performance across various tasks, but current training approaches combine cross-entropy loss with extensive data, human feedback, or ad hoc methods to enhance performance. |
| Approach: | They evaluate the use of established semantic segmentation loss functions in natural language generation to create a versatile, practical, and scalable solution for fine-tuning different architectures. |
| Outcome: | The proposed models improve on Math Word Problems and question answering tasks without additional data or human feedback. |
Distractor Generation Using Generative and Discriminative Capabilities of Transformer-based Models (2024.lrec-main)
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| Challenge: | Multiple Choice Questions (MCQs) are used to test language learners' comprehension and knowledge. |
| Approach: | They propose an automatic distractor generation approach which generates correct and incorrect answer options and then discriminates potential correct options from distractors. |
| Outcome: | The proposed approach outperforms previous models on multiple choice questions and reading comprehension questions. |
Using LLMs to simulate students’ responses to exam questions (2024.findings-emnlp)
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Luca Benedetto, Giovanni Aradelli, Antonia Donvito, Alberto Lucchetti, Andrea Cappelli, Paula Buttery
| Challenge: | Existing studies have used Large Language Models to simulate students answering exam questions . a proposed prompt for GPT-3.5 is not suitable for all LLMs, and there is no correlation between the quality of the rationales obtained with the model and the accuracy of the student simulation task. |
| Approach: | They propose a large language model prompt engineered for GPT-3.5 that can be used to answer exam questions simulating students of different skill levels. |
| Outcome: | The proposed prompt is robust to different educational domains and generalise to data unseen during prompt engineering phase. |
Beyond Names: How Grammatical Gender Markers Bias LLM-based Educational Recommendations (2026.eacl-long)
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| Challenge: | grammatical gender cues alone trigger substantial distributional shifts in educational recommendations . authors show that up to 76% of the bias exhibited when using prompts with proper names is already present with grammatical gender markers alone. |
| Approach: | They investigate gender biases exhibited by LLM-based virtual assistants in Italian . they show that simply changing noun and adjective endings significantly shifts recommendations . |
| Outcome: | The findings highlight the need for robust bias evaluation and mitigation strategies before deploying LLM-based virtual assistants in student-facing contexts. |