What are the limitations of LLMs?
Research Question
Academic Insights
Large Language Models (LLMs) exhibit impressive capabilities in language understanding and generation, but they face significant limitations in semantic understanding, cultural commonsense, and energy efficiency.
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Semantic Understanding Limitations:
- LLMs struggle to capture fundamental semantic properties such as semantic entailment and consistency, which are crucial for deep linguistic understanding .
- They are unable to learn concepts beyond the first level of the Borel Hierarchy, limiting their ability to fully grasp linguistic meaning .
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Cultural Commonsense Challenges:
- LLMs show a significant discrepancy in performance when dealing with culture-specific commonsense knowledge, indicating an inherent bias in their cultural understanding .
- The language used to query LLMs can impact their performance on culturally related tasks, further complicating their application across diverse cultural contexts .
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Energy Efficiency and Deployment Issues:
- The substantial size of LLMs presents challenges in deployment, inference, and training, leading to high energy consumption and carbon emissions .
- Efforts like DynamoLLM aim to optimize energy and cost efficiency in LLM inference environments, but the dynamic nature of these environments creates a complex search space for system configurations .
Conclusion
While LLMs have advanced the field of NLP significantly, their limitations in semantic understanding, cultural commonsense, and energy efficiency highlight the need for continued research and development to address these challenges and improve their applicability across diverse contexts.
Understanding the Capabilities and Limitations of Large Language Models for Cultural Commonsense
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Related Questions
- How do LLMs compare to traditional models?
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- What ethical concerns are associated with LLMs?
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