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Found 3 papers, 1 papers with code
Context-dependent Instruction Tuning for Dialogue Response Generation
Recent language models have achieved impressive performance in natural language tasks by incorporating instructions with task input during fine-tuning.
Knowledge Graph-Augmented Language Models for Knowledge-Grounded Dialogue Generation
To overcome this limitation, we propose SUbgraph Retrieval-augmented GEneration (SURGE), a framework for generating context-relevant and knowledge-grounded dialogues with the KG.
Language Detoxification with Attribute-Discriminative Latent Space
Transformer-based Language Models (LMs) have achieved impressive results on natural language understanding tasks, but they can also generate toxic text such as insults, threats, and profanity, limiting their real-world applications.
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