Papers by Juan Diego Rodriguez

5 papers
KRISTEVA: Close Reading as a Novel Task for Benchmarking Interpretive Reasoning (2025.acl-long)

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Challenge: a study of close reading skills in large language models (LLMs) shows that LLMs still lag behind human evaluators on 10 of 11 tasks.
Approach: They propose a benchmark to evaluate close reading skills in large language models . they propose three tasks to approximate different elements of the close reading process .
Outcome: The proposed benchmarks show that state-of-the-art LLMs possess some college-level close reading competency, but performance still trails human evaluators on 10 out of 11 tasks.
Cross-Domain Detection of GPT-2-Generated Technical Text (2022.naacl-main)

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Challenge: Recent advances in techniques for generating realistic synthetic content pose a diverse set of problems with significant societal consequences.
Approach: They propose to use paragraph-level detectors to detect tampering of full-length documents under a variety of threat models to detect machine-generated text.
Outcome: The proposed detectors can detect the tampering of full-length documents under a variety of threat models.
Leveraging WordNet Paths for Neural Hypernym Prediction (2020.coling-main)

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Challenge: Existing work on lexical relations based on distributed representations has differed widely.
Approach: They propose a model that generates taxonomy paths for hypernym prediction using WordNet sequences.
Outcome: The hypo2path model outperforms the best model by 4.11 points in hit-at-one (H@1) The proposed model outpersforms previous models by a factor of 0.9.
Characterizing the Role of Similarity in the Property Inferences of Language Models (2025.naacl-long)

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Challenge: Property inheritance is a phenomenon where novel properties are projected from higher level categories to lower level ones.
Approach: They investigate how LMs perform property inheritance with behavioral and causal analysis experiments.
Outcome: The results provide insight into the conceptual structure of language models and may suggest new psycholinguistic experiments for human subjects.
Transfer Learning for Entity Recognition of Novel Classes (C18-1)

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Challenge: Existing approaches to entity recognition are based on class labels in source and target domains, and many NER corpora only annotate a small number of categories.
Approach: They replicate and extend several past studies on transfer learning for entity recognition.
Outcome: The proposed methods perform better when there is more labeled target data.

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