REALISTA generates semantically coherent adversarial prompts via latent-space optimization over input-dependent editing directions, achieving stronger hallucination elicitation than prior realistic attacks on open-source and reasoning LLMs.
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3 Pith papers cite this work, alongside 54 external citations. Polarity classification is still indexing.
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The work develops a reflective LLM-based storytelling agent for older adults that integrates argumentation schemes and argument mining with knowledge graphs and user modeling to generate and inspect personalized health narratives, evaluated through expert design and user studies showing recognition,
ARLtR is a framework for jointly constructing knowledge graphs, embeddings, and grounded QA pairs from text, released as a Roman Empire dataset with over 19,000 entities and 8,400 QA pairs.
citing papers explorer
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REALISTA: Realistic Latent Adversarial Attacks that Elicit LLM Hallucinations
REALISTA generates semantically coherent adversarial prompts via latent-space optimization over input-dependent editing directions, achieving stronger hallucination elicitation than prior realistic attacks on open-source and reasoning LLMs.
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A Reflective Storytelling Agent for Older Adults: Integrating Argumentation Schemes and Argument Mining in LLM-Based Personalised Narratives
The work develops a reflective LLM-based storytelling agent for older adults that integrates argumentation schemes and argument mining with knowledge graphs and user modeling to generate and inspect personalized health narratives, evaluated through expert design and user studies showing recognition,
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All Relations Lead to Rome: Automated Knowledge Graph Creation and Question Generation
ARLtR is a framework for jointly constructing knowledge graphs, embeddings, and grounded QA pairs from text, released as a Roman Empire dataset with over 19,000 entities and 8,400 QA pairs.