Papers with GPT-based models
Can Uniform Meaning Representation Help GPT-4 Translate from Indigenous Languages? (2025.acl-short)
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| Challenge: | In this study, we examine the downstream utility of Uniform Meaning Representation (UMR) for low-resource languages. |
| Approach: | They explore the utility of Uniform Meaning Representation (UMR) for low-resource languages by incorporating it into GPT-4 prompts. |
| Outcome: | The proposed model performs better than existing models in Navajo, Arápaho, and Kukama with and without demonstrations and annotations. |
AutoBool: Reinforcement-Learned LLM for Effective Automatic Systematic Reviews Boolean Query Generation (2026.eacl-long)
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| Challenge: | Existing approaches to generate Boolean queries for systematic reviews are limited by the lack of ground-truth best Boolesan queries. |
| Approach: | They propose a reinforcement learning framework that trains large language models to generate effective Boolean queries for medical systematic reviews. |
| Outcome: | The proposed framework outperforms zero-shot/few-shot prompting on 65 588 topics . it also matches or exceeds the effectiveness of larger GPT-based models using smaller backbones . |
CONAN-MT-SP: A Spanish Corpus for Counternarrative Using GPT Models (2024.lrec-main)
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María Estrella Vallecillo Rodríguez, Maria Victoria Cantero Romero, Isabel Cabrera De Castro, Arturo Montejo Ráez, María Teresa Martín Valdivia
| Challenge: | a new study evaluates the performance of GPT-based models to generate CNs for hate speech in Spanish . a growing number of social interactions through digital platforms have led to inappropriate behavior . |
| Approach: | They propose to use GPT-based models to generate CNs for Hate Speech in Spanish . they use the DeepL API to automatically translate the HS segment into Spanish based on the original CN pairs translated into spanish . |
| Outcome: | The proposed models outperform human models in most instances, the authors say . the results will be made available to the research community . |
Towards Explainable Hate Speech Detection (2025.findings-acl)
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| Challenge: | Recent advances in deep learning have significantly enhanced the efficiency and accuracy of natural language processing (NLP) tasks. |
| Approach: | They propose a model that uses valence, arousal, and dominance (VAD) scores to detect hate speech and a weighted sum of valent, valance, and valency (VA) scores for classification. |
| Outcome: | The proposed model can compete with state-of-the-art models in detecting hate speech and non-hate speech words based on their individual and summed VAD-values. |
Tricking LLMs into Disobedience: Formalizing, Analyzing, and Detecting Jailbreaks (2024.lrec-main)
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| Challenge: | Existing methods to jailbreak large language models have been poorly studied . a recent study showed that non-expert users can jailbreak LLMs by manipulating their prompts . |
| Approach: | They propose a formalism and a taxonomy of known (and possible) jailbreaks . they propose generating a dataset of model outputs across 3700 jailbreak prompts a 'prompt' attack is a new attack popularly categorized as "prompting injection attacks" |
| Outcome: | The proposed model exploits 3700 jailbreak prompts over 4 tasks to analyze their effectiveness . authors show that the model can learn to perform a new task on unseen examples . |