Summary of The Strong Pull Of Prior Knowledge in Large Language Models and Its Impact on Emotion Recognition, by Georgios Chochlakis et al.
The Strong Pull of Prior Knowledge in Large Language Models and Its Impact on Emotion Recognition
by Georgios Chochlakis, Alexandros Potamianos, Kristina Lerman, Shrikanth Narayanan
First submitted to arxiv on: 25 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper explores In-context Learning (ICL) with Large Language Models (LLMs), a paradigm that enables natural language tasks without updating model parameters. ICL’s promise is to adapt models to perform tasks efficiently, leveraging background knowledge of the task or task priors. However, recent work reveals that LLMs struggle to integrate information from demonstrations that contrast task priors, leading to performance saturation and suboptimal levels. The study proposes experiments and measurements to quantify consistency of LLM priors and their impact on posteriors. Findings show that LLMs have strong yet inconsistent priors in emotion recognition tasks, which ossify predictions. Larger models exhibit stronger effects, emphasizing the need for caution when using ICL with larger LLMs outside their pre-training domain. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ICL is a new way to use Large Language Models (LLMs) without updating them. Instead of learning from scratch, these models can adapt to perform tasks quickly and efficiently. But, they might not always get it right. Researchers found that when these models try to recognize emotions in text, they can be too confident in their answers and don’t change even if shown new information. This is because the larger LLMs have strong preconceptions about how emotions work, which they stick to instead of updating their knowledge. The study warns that we should be careful using this method with very large models for tasks like recognizing emotions outside what they were trained on. |
