7 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…
Do We Still Need Humans in the Loop? Comparing Human and LLM Annotation in Active Learning for Hostility Detection
Ahmad Dawar Hakimi, Lea Hirlimann, Isabelle Augenstein +1
Instruction-tuned LLMs can annotate thousands of instances at low cost. This raises two questions for active learning (AL): can LLM labels replace human labels within the AL loop,…
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…
Analysing Differences in Persuasive Language in LLM-Generated Text: Uncovering Stereotypical Gender Patterns
Amalie Brogaard Pauli, Maria Barrett, Max Müller-Eberstein +2
Large language models (LLMs) are increasingly used for everyday communication tasks, including drafting interpersonal messages intended to influence and persuade. Prior work has sh…
Mitigating Cross-Lingual Cultural Inconsistencies in LLMs via Consensus-Driven Preference Optimisation
Lucas Resck, Isabelle Augenstein, Anna Korhonen
Despite their impressive capabilities, multilingual large language models (MLLMs) frequently exhibit inconsistent behaviour when the prompt's language changes. While such adaptatio…