Papers by Domenic Rosati
LLMs Underperform Graph-Based Parsers on Supervised Relation Extraction for Complex Graphs (2026.acl-short)
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| Challenge: | Relation extraction is a core NLP task which involves extracting [head, relation, dependent] RDF triples from text. |
| Approach: | They evaluate four large language models against a graph-based parser on six relation extraction datasets with sentence graphs of varying sizes and complexities. |
| Outcome: | The graph-based parser outperforms the LLMs on six relation extraction datasets with sentence graphs of varying sizes and complexities. |
Mixture of Soft Prompts for Controllable Data Generation (2023.findings-emnlp)
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| Challenge: | Large language models (LLMs) generate fluent text when the target output follows natural language patterns. |
| Approach: | They propose a method that uses large language models to generate fluent text from a limited ontology rather than direct prediction by using soft prompts. |
| Outcome: | The proposed method produces diverse and natural text while preserving label semantics. |
Not Lost After All: How Cross-Encoder Attribution Challenges Position Bias Assumptions in LLM Summarization (2025.findings-emnlp)
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| Challenge: | Position bias is a key limitation in automatic summarization. |
| Approach: | They propose a cross-encoder-based alignment method that processes summary-source sentence pairs . |
| Outcome: | The proposed method allows better identification of semantic correspondences even when summaries substantially rewrite the source. |
Immunization against harmful fine-tuning attacks (2024.findings-emnlp)
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| Challenge: | Large Language Models are often trained with safety guards to prevent harmful text generation. |
| Approach: | They propose a formal framework based on the training budget of an attacker to validate defenses against harmful fine-tuning attacks. |
| Outcome: | The proposed framework validates whether a model has been fine-tuned against harmful fine-uning attacks on harmful datasets. |
Using contradictions improves question answering systems (2023.acl-short)
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| Challenge: | Existing systems that use contradiction to determine if a question is supported by background contexts do better than those that use entailment. |
| Approach: | They propose a method that incorporates contradiction in natural language inference (NLI) they propose to reformulate answers from QA systems as hypotheses and then select the best one based on the results. |
| Outcome: | The proposed method improves on multiple choice and extractive QA in two settings. |
Long-form evaluation of model editing (2024.naacl-long)
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| Challenge: | Existing evaluations of model editing only use the ‘next few tokens’ completions after a prompt. |
| Approach: | They propose a new evaluation protocol that measures the efficacy and impact of model editing in long-form generative settings by using a machine-rated survey and a classifier which correlates well with human ratings. |
| Outcome: | The proposed evaluation protocol has little relationship with short-form metrics despite being designed to extend efficacy, generalization, locality, and portability into a long-form setting. |