\n ## Bar Chart: Mean Ratio of Token Types by Model ### Overview This bar chart compares the mean ratio of different token types (digit, operator, conjunction, and other) for two language models: Qwen2.5-7B-Math and Llama3.1-8B-Instruct. The chart uses grouped bars to represent each token type within each model. ### Components/Axes * **X-axis:** Model Name (Qwen2.5-7B-Math, Llama3.1-8B-Instruct) * **Y-axis:** Mean\_ratio (ranging from 0.00 to 0.14) * **Legend:** * digit (light green) * operator (pale green) * conjunction (light blue) * other (pale blue) ### Detailed Analysis The chart consists of two groups of four bars, one for each model. Within each group, each bar represents the mean ratio for a specific token type. **Qwen2.5-7B-Math:** * **digit:** The light green bar for 'digit' is approximately 0.065. * **operator:** The pale green bar for 'operator' is approximately 0.055. * **conjunction:** The light blue bar for 'conjunction' is approximately 0.050. * **other:** The pale blue bar for 'other' is approximately 0.048. **Llama3.1-8B-Instruct:** * **digit:** The light green bar for 'digit' is approximately 0.135. * **operator:** The pale green bar for 'operator' is approximately 0.115. * **conjunction:** The light blue bar for 'conjunction' is approximately 0.105. * **other:** The pale blue bar for 'other' is approximately 0.090. ### Key Observations * Llama3.1-8B-Instruct consistently exhibits higher mean ratios across all token types compared to Qwen2.5-7B-Math. * For both models, the 'digit' token type has the highest mean ratio, followed by 'operator', 'conjunction', and 'other'. * The difference in mean ratios between the two models is most pronounced for the 'digit' token type. ### Interpretation The data suggests that Llama3.1-8B-Instruct is more inclined to generate or process text containing digits, operators, conjunctions, and other token types compared to Qwen2.5-7B-Math. This could indicate that Llama3.1-8B-Instruct is better suited for tasks involving mathematical reasoning or code generation, where these token types are more prevalent. The consistently higher ratios across all categories suggest a fundamental difference in the models' token distribution preferences. The large difference in the 'digit' category is particularly noteworthy, potentially indicating a stronger mathematical capability in Llama3.1-8B-Instruct. The chart provides a quantitative comparison of the token type distributions, offering insights into the models' strengths and weaknesses.