Luca Benedetto

About Luca Benedetto

Luca Benedetto, With an exceptional h-index of 7 and a recent h-index of 7 (since 2020), a distinguished researcher at Politecnico di Milano, specializes in the field of NLP, Machine Learning, Artificial Intelligence in Education, Language Models.

His recent articles reflect a diverse array of research interests and contributions to the field:

On the application of Large Language Models for language teaching and assessment technology

A quantitative study of NLP approaches to question difficulty estimation

A survey on recent approaches to question difficulty estimation from text

The Cambridge Multiple-Choice Questions Reading Dataset

An assessment of recent techniques for question difficulty estimation from text

Complexity-based partitioning of CSFI problem instances with Transformers

On the application of Transformers for estimating the difficulty of Multiple-Choice Questions from text

Towards the application of calibrated Transformers to the unsupervised estimation of question difficulty from text

Luca Benedetto Information

University

Position

PhD fellow

Citations(all)

135

Citations(since 2020)

135

Cited By

13

hIndex(all)

7

hIndex(since 2020)

7

i10Index(all)

6

i10Index(since 2020)

6

Email

University Profile Page

Google Scholar

Luca Benedetto Skills & Research Interests

NLP

Machine Learning

Artificial Intelligence in Education

Language Models

Top articles of Luca Benedetto

A quantitative study of NLP approaches to question difficulty estimation

2023/6/30

Luca Benedetto
Luca Benedetto

H-Index: 2

A survey on recent approaches to question difficulty estimation from text

2023/1/13

The Cambridge Multiple-Choice Questions Reading Dataset

2023

An assessment of recent techniques for question difficulty estimation from text

2022/2/16

Luca Benedetto
Luca Benedetto

H-Index: 2

Complexity-based partitioning of CSFI problem instances with Transformers

arXiv preprint arXiv:2106.14481

2021/6/28

On the application of Transformers for estimating the difficulty of Multiple-Choice Questions from text

2021/4

Towards the application of calibrated Transformers to the unsupervised estimation of question difficulty from text

2021

Introducing a framework to assess newly created questions with Natural Language Processing

2020/7

Luca Benedetto
Luca Benedetto

H-Index: 2

Paolo Cremonesi
Paolo Cremonesi

H-Index: 25

R2DE: a NLP approach to estimating IRT parameters of newly generated questions

2020/3

Luca Benedetto
Luca Benedetto

H-Index: 2

Paolo Cremonesi
Paolo Cremonesi

H-Index: 25

See List of Professors in Luca Benedetto University(Politecnico di Milano)

Co-Authors

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