Summary of Talk Less, Interact Better: Evaluating In-context Conversational Adaptation in Multimodal Llms, by Yilun Hua and Yoav Artzi
Talk Less, Interact Better: Evaluating In-context Conversational Adaptation in Multimodal LLMs
by Yilun Hua, Yoav Artzi
First submitted to arxiv on: 2 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The study explores whether multimodal large language models (MLLMs) can adapt their communication efficiency during interactions, similar to humans. The authors introduce an automated framework called ICCA to evaluate this conversational adaptation in MLLMs. They test several state-of-the-art MLLMs and find that while they understand increasingly efficient language from their interlocutor, they do not spontaneously make their own language more efficient over time. However, some models (e.g., GPT-4) can be prompted to exhibit this behavior. The study highlights the limitations of current training regimes in capturing human-like linguistic interactions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are like super-smart computers that can understand and generate human-like text. Scientists have long been fascinated by how humans talk to each other, noticing that we often make our language more efficient as we chat. Can these powerful computers do the same? Researchers developed a tool called ICCA to test if large language models can adapt their communication style during conversations. They tried several top-performing models and found that while they’re great at understanding efficient language from others, they don’t naturally make their own language more efficient. However, some models could be “tricked” into doing so with extra guidance. This study shows how far we are from creating computers that mimic human-like conversations. |
Keywords
* Artificial intelligence * Gpt
