5 papers · 1 filter
Evaluating LLMs on Generating Age-Appropriate Child-Like Conversations
Syed Zohaib Hassan, Pål Halvorsen, Miriam S. Johnson +1
Large Language Models (LLMs), predominantly trained on adult conversational data, face significant challenges when generating authentic, child-like dialogue for specialized applica…
Stronger Re-identification Attacks through Reasoning and Aggregation
Lucas Georges Gabriel Charpentier, Pierre Lison
Text de-identification techniques are often used to mask personally identifiable information (PII) from documents. Their ability to conceal the identity of the individuals mentione…
Re-identification of De-identified Documents with Autoregressive Infilling
Lucas Georges Gabriel Charpentier, Pierre Lison
Documents revealing sensitive information about individuals must typically be de-identified. This de-identification is often done by masking all mentions of personally identifiable…
Enhancing Naturalness in LLM-Generated Utterances through Disfluency Insertion
Syed Zohaib Hassan, Pierre Lison, Pål Halvorsen
Disfluencies are a natural feature of spontaneous human speech but are typically absent from the outputs of Large Language Models (LLMs). This absence can diminish the perceived na…
Retrieval-Augmented Neural Response Generation Using Logical Reasoning and Relevance Scoring
Nicholas Thomas Walker, Stefan Ultes, Pierre Lison
Constructing responses in task-oriented dialogue systems typically relies on information sources such the current dialogue state or external databases. This paper presents a novel…