Papers with annotation-free framework
TextBind: Multi-turn Interleaved Multimodal Instruction-following in the Wild (2024.findings-acl)
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| Challenge: | Large language models with instruction-following capabilities have revolutionized the field of artificial intelligence. |
| Approach: | They propose an annotation-free framework for empowering large language models with instruction-following capabilities. |
| Outcome: | The proposed framework generates multi-turn multimodal instruction-response conversations from a language model. |
Enhancing Persona Consistency for LLMs’ Role-Playing using Persona-Aware Contrastive Learning (2025.findings-acl)
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| Challenge: | Existing methods for analyzing and analyzing large language models (LLMs) lack of emotion and fine-grained role awareness limits the model’s ability to provide personalized and diverse interactions further. |
| Approach: | They propose an annotation-free framework to align LLMs’ behavior during role-playing, enhancing the model’s role consistency. |
| Outcome: | The proposed framework outperforms vanilla LLMs under automatic evaluation methods and human expert evaluation. |