Papers by David Garcia
The iRead4Skills Intelligent Complexity Analyzer (2025.emnlp-demos)
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Wafa Aissa, Raquel Amaro, David Antunes, Thibault Bañeras-Roux, Jorge Baptista, Alejandro Catala, Luís Correia, Thomas François, Marcos Garcia, Mario Izquierdo-Álvarez, Nuno Mamede, Vasco Martins, Miguel Neves, Eugénio Ribeiro, Sandra Rodriguez Rey, Elodie Vanzeveren
| Challenge: | 20% of EU adult population exhibits low-literacy and numeracy skills (EA, 2021). |
| Approach: | iRead4Skills Intelligent Complexity Analyzer integrates a range of NLP components to assess input texts along multiple levels of granularity and linguistic dimensions in Portuguese, Spanish, and French. |
| Outcome: | The system assigns four tailored difficulty levels and introduces four diagnostic yardsticks—textual structure, lexicon, syntax, and semantics—offering users actionable feedback on specific dimensions of textual complexity. |
Only a Little to the Left: A Theory-grounded Measure of Political Bias in Large Language Models (2025.acl-long)
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| Challenge: | Political biases in language models can affect performance in many applications . political biased models are often left-leaning, but are generally more left- leaning for instruction-tuned models . |
| Approach: | They propose to use the Political Compass Test to measure political bias in language models . they use survey-based evaluation tools to test prompts and classify their political stances . |
| Outcome: | The proposed model is based on the Political Compass Test, but is not scientifically valid. |
Transcending Scaling Laws with 0.1% Extra Compute (2023.emnlp-main)
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Yi Tay, Jason Wei, Hyung Chung, Vinh Tran, David So, Siamak Shakeri, Xavier Garcia, Steven Zheng, Jinfeng Rao, Aakanksha Chowdhery, Denny Zhou, Donald Metzler, Slav Petrov, Neil Houlsby, Quoc Le, Mostafa Dehghani
| Challenge: | Existing scaling of language models is expensive and requires significant computational costs. |
| Approach: | They propose a method that substantially improves existing language models and their scaling curves with a relatively tiny amount of extra compute. |
| Outcome: | The proposed method significantly improves existing language models and their scaling curves with a relatively tiny amount of extra compute. |
Missing the Margins: A Systematic Literature Review on the Demographic Representativeness of LLMs (2025.findings-acl)
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| Challenge: | 211 studies on the demographic representativeness of large language models have conflicting results . 29% of the studies report positive conclusions on the representativeness, 30% do not evaluate LLMs across multiple demographic categories or within demographic subcategories. |
| Approach: | 211 papers review the representativeness of large language models . authors recommend more precise evaluation methods and comprehensive documentation of demographic attributes . |
| Outcome: | 211 studies on the representativeness of large language models are reviewed . 29% of the studies report positive conclusions, but 30% fail to specify subcategories . authors recommend more precise evaluation methods and documentation of demographic attributes . |