Papers by Ignacio Castro
Efficient Solutions For An Intriguing Failure of LLMs: Long Context Window Does Not Mean LLMs Can Analyze Long Sequences Flawlessly (2025.coling-main)
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| Challenge: | Large Language Models (LLMs) have demonstrated remarkable capabilities in comprehending and analyzing lengthy sequential inputs. |
| Approach: | They propose to implement ad-hoc solutions that enhance LLMs’ performance on long input sequences by up to 50% while reducing API cost and latency by up . to address this limitation, they propose to use three datasets and two tasks to analyze news categorization and sentence analysis to evaluate their models. |
| Outcome: | The proposed solutions significantly improve LLMs’ performance on long input sequences by up to 50% while reducing API cost and latency by up . to 93% and 50%, respectively. |
LEDA: a Large-Organization Email-Based Decision-Dialogue-Act Analysis Dataset (2023.findings-acl)
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Mladen Karan, Prashant Khare, Ravi Shekhar, Stephen McQuistin, Ignacio Castro, Gareth Tyson, Colin Perkins, Patrick Healey, Matthew Purver
| Challenge: | Using dialog acts to study decision-making in large distributed organizations is challenging due to the size and distributed nature of such groups. |
| Approach: | They propose a set of dialog acts for the study of decision-making mechanisms in large distributed organizations. |
| Outcome: | The proposed dataset can be used to better understand decision-making in large distributed organizations. |
Tracing Linguistic Markers of Influence in a Large Online Organisation (2023.acl-short)
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Prashant Khare, Ravi Shekhar, Mladen Karan, Stephen McQuistin, Colin Perkins, Ignacio Castro, Gareth Tyson, Patrick Healey, Matthew Purver
| Challenge: | Social science and psycholinguistic research have shown that power and status affect how people use language in a range of domains. |
| Approach: | They propose to use lexical categories and BERT to predict levels of influence in an online community and identify key linguistic differences between people before and after becoming influential. |
| Outcome: | The results show that participants' levels of influence can be predicted from their email text, and identify key differences in language use for the same person before and after becoming influential. |
A Dataset for Expert Reviewer Recommendation with Large Language Models as Zero-shot Rankers (2025.coling-main)
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Vanja M. Karan, Stephen McQuistin, Ryo Yanagida, Colin Perkins, Gareth Tyson, Ignacio Castro, Patrick G.T. Healey, Matthew Purver
| Challenge: | state of the art reviewer recommendation systems still have relatively high error rates . |
| Approach: | They propose to use a large language model to improve on SotA, but not a cure-all . they first create a new dataset and introduce LLMs with prompting to evaluate their performance. |
| Outcome: | The proposed approach improves on SotA but not cure-all, the authors argue . they show that the proposed approach can be extended to many related tasks . |