\n ## Bar Chart: Truthfulness Evaluation of Language Models ### Overview This bar chart compares the truthfulness of different language models (Llama-2 7B, Llama-2 13B, Llama-2 70B, GPT-3.5-turbo, GPT-4, Gemini Pro) across various evaluation datasets (TruthfulQA, HellaSwag, MMLU, ARC-Challenge, OpenBookQA). The chart displays the percentage of truthful answers generated by each model on each dataset. ### Details * **X-axis:** Language Models (Llama-2 7B, Llama-2 13B, Llama-2 70B, GPT-3.5-turbo, GPT-4, Gemini Pro) * **Y-axis:** Percentage of Truthful Answers (%) * **Bars:** Represent the performance of each model on each dataset. Each model has a set of bars, one for each dataset. * **Datasets:** * TruthfulQA: Measures the model's ability to avoid generating false statements. * HellaSwag: Tests commonsense reasoning. * MMLU: Measures massive multitask language understanding. * ARC-Challenge: Assesses reasoning about science questions. * OpenBookQA: Tests open-book question answering. ### Observations * GPT-4 generally exhibits the highest percentage of truthful answers across most datasets. * Gemini Pro shows competitive performance, often close to GPT-4. * Llama-2 70B performs better than Llama-2 13B and Llama-2 7B, indicating that model size impacts truthfulness. * The performance varies significantly depending on the dataset, suggesting that truthfulness is context-dependent. ### Table of Results (Example) | Model | TruthfulQA (%) | HellaSwag (%) | MMLU (%) | ARC-Challenge (%) | OpenBookQA (%) | |--------------|----------------|---------------|----------|-------------------|-----------------| | Llama-2 7B | 45 | 60 | 55 | 30 | 40 | | Llama-2 13B | 50 | 65 | 60 | 35 | 45 | | Llama-2 70B | 60 | 75 | 70 | 45 | 55 | | GPT-3.5-turbo| 70 | 80 | 75 | 50 | 60 | | GPT-4 | 85 | 90 | 85 | 65 | 75 | | Gemini Pro | 80 | 88 | 82 | 60 | 70 | ```