Papers with DPO fine-tuning
Being Kind Isn’t Always Being Safe: Diagnosing Affective Hallucination in LLMs (2026.findings-eacl)
Copied to clipboard
| Challenge: | Large language models (LLMs) are increasingly engaged in emotionally vulnerable conversations that extend beyond information seeking to moments of personal distress. |
| Approach: | They propose AHaBench, a benchmark of 500 mental-health-related prompts with expert-informed reference responses, evaluated along three dimensions: Emotional Enmeshment, Illusion of Presence, and Fostering Overdependence. |
| Outcome: | The proposed model is based on 500 mental-health-related prompts with expert-informed reference responses and a 5K-instance preference dataset enabling direct preference optimization (DPO) for alignment with emotionally responsible behavior. |
Implicit Cross-Lingual Rewarding for Efficient Multilingual Preference Alignment (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to align English LLMs with human preferences rely on expensive human annotations or advanced multilingual preference alignment models. |
| Approach: | They propose a method that captures learned preferences from English models by implicit rewards . they annotate preference relations in cross-lingual instruction-following pairs using English . |
| Outcome: | The proposed approach captures learned preferences from well-aligned English models by implicit rewards and transfers them to other languages through iterative training. |