LLM for Comparative Narrative Analysis
In this paper, we conducted a Multi-Perspective Comparative Narrative Analysis (CNA) on three prominent LLMs: GPT-3.5, PaLM2, and Llama2. We applied identical prompts and evaluated their outputs on specific tasks, ensuring an equitable and unbiased comparison between various LLMs. Our study revealed that the three LLMs generated divergent responses to the same prompt, indicating notable discrepancies in their ability to comprehend and analyze the given task. Human evaluation was used as the gold standard, evaluating four perspectives to analyze differences in LLM performance.
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Methods
Adam •
Attention Dropout •
BPE •
Cosine Annealing •
Dense Connections •
Dropout •
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Fixed Factorized Attention •
GELU •
GPT-3 •
Layer Normalization •
Linear Layer •
Linear Warmup With Cosine Annealing •
Multi-Head Attention •
Residual Connection •
Softmax •
Strided Attention •
Weight Decay
