Summary of Annotation Guidelines-based Knowledge Augmentation: Towards Enhancing Large Language Models For Educational Text Classification, by Shiqi Liu et al.
Annotation Guidelines-Based Knowledge Augmentation: Towards Enhancing Large Language Models for Educational Text Classification
by Shiqi Liu, Sannyuya Liu, Lele Sha, Zijie Zeng, Dragan Gasevic, Zhi Liu
First submitted to arxiv on: 3 Jun 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 Medium Difficulty Summary: The paper proposes the Annotation Guidelines-based Knowledge Augmentation (AGKA) approach to improve Large Language Models (LLMs) for learning engagement classification (LEC). AGKA retrieves label definition knowledge from annotation guidelines and applies a random under-sampler to select typical examples. The study evaluates LEC on six datasets, covering behavior, emotion, and cognition classification tasks. Results show that AGKA enhances non-fine-tuned LLMs, particularly GPT 4.0 and Llama 3 70B. While GPT 4.0 with AGKA outperforms fine-tuned models on simple binary classification, it lags in multi-class tasks requiring complex semantic understanding. Llama 3 70B with AGKA is a promising combination, matching the performance of closed-source GPT 4.0 with AGKA. The paper also highlights the challenges faced by LLMs when distinguishing between labels with similar names. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty Summary: This study uses special computer models to help teachers understand how students are learning. These models can analyze text, like essays or chat logs, and identify what’s going on in students’ minds. Researchers wanted to see if these models could get better at this task by using specific guidelines and a few good examples. They tested the models on six different tasks, like identifying emotions or understanding opinions. The results show that these models can improve when given the right guidance. However, they struggle with more complex tasks, where they need to understand subtle meanings. |
Keywords
» Artificial intelligence » Classification » Gpt » Llama
