4 papers
Output Vector Editing for Memorization Mitigation in Large Language Models
Ahmad Dawar Hakimi, Kaiwei Lei, Isabelle Augenstein +1
Large language models memorize and reproduce sequences from their training data, creating privacy, copyright, and security risks. Existing neuron-level mitigation methods equate ed…
Emergence of Context Characteristics Sensitivity in Large Language Models
Nadya Yuki Wangsajaya, Haeun Yu, Isabelle Augenstein
During instruction fine-tuning (IFT), large language models (LLMs) learn to follow instructions by using the provided context to answer a query. While prior work has studied how co…
Whose Norms? Disentangling Cultural and Personal Alignment in Large Language Models
Angana Borah, Isabelle Augenstein, Rada Mihalcea
Large language models are increasingly used for social decision-making situations that require balancing cultural norms with personal preferences. For example, a user preferring ho…
Mind the Style Gap: Meta-Evaluation of Style and Attribute Transfer Metrics
Amalie Brogaard Pauli, Isabelle Augenstein, Ira Assent
Large language models (LLMs) make it easy to rewrite a text in any style -- e.g. to make it more polite, persuasive, or more positive -- but evaluation thereof is not straightforwa…