Small LLMs Struggle with Label Flipping in In-Context Learning
Analysis
This ArXiv paper examines the limitations of small language models in in-context learning scenarios. The research highlights a challenge where these models fail to adapt effectively when labels are changed within the context.
Key Takeaways
- •Small LLMs may struggle to generalize and adapt to label changes in in-context learning.
- •The research likely focuses on the semantic understanding and reasoning capabilities of these models.
- •This finding suggests limitations in the flexibility and adaptability of smaller models.
Reference
“The paper likely investigates the performance of small LLMs in a context where the expected output label needs to be dynamically adjusted based on the given context.”