Can Large Language Models Understand Context?
Summary:
Understanding context is crucial for comprehending human language, and Large Language Models (LLMs) have demonstrated impressive capabilities in this area. However, limited attention has been paid to probing their linguistic capability of understanding contextual features. This paper introduces a context understanding benchmark with four distinct tasks and nine datasets to evaluate the models’ ability to understand context. The experimental results indicate that pre-trained dense models struggle with understanding nuanced contextual features compared to fine-tuned models. Additionally, the paper evaluates the context understanding of quantized models under in-context-learning settings and finds varying degrees of performance reduction on the benchmark.
Major Findings:
- Pre-trained dense models struggle with understanding nuanced contextual features compared to fine-tuned models.
- Quantized models show varying degrees of performance reduction on the context understanding benchmark.
- Larger models exhibit promising performance on certain tasks, indicating their effectiveness in handling coreference relations and discourse parsing.
Analysis and Critique:
- The paper provides a comprehensive evaluation of LLMs’ context understanding capabilities, highlighting the challenges and limitations of pre-trained dense models in understanding nuanced contextual features.
- The study introduces a context understanding benchmark, but it has limitations in evaluating other LLMs designed for longer input scenarios and languages other than English.
- The reliability of the experiment results is addressed, acknowledging the challenges posed by limited time, budget, and computing resources in running multiple rounds for every experiment.
Appendix
| Model | gpt-3.5-turbo-1106 |
| Date Generated | 2024-02-26 |
| Abstract | https://arxiv.org/abs/2402.00858v1 |
| HTML | https://browse.arxiv.org/html/2402.00858v1 |
| Truncated | False |
| Word Count | 6969 |