## Bar Chart: Accuracy on f1 Score for Different Models and Tasks
### Overview
This bar chart compares the accuracy (f1 score) of several large language models (LLMs) – GPT-03-mini, QwQ-32B, Gemini-2.0, and DeepSeek-R1-70B – across four different tasks: Movement, Extension, Recolour, and Others. Each model is evaluated using two prompting methods: RSPC and KAAR. The chart displays the accuracy as a percentage on the y-axis, and the tasks on the x-axis. Each bar is segmented to show the performance of each model/prompting method combination. Total counts for each task are provided below the x-axis labels.
### Components/Axes
* **X-axis:** Tasks - Movement, Extension, Recolour, Others.
* **Y-axis:** Accuracy on f1 (%) - Scale ranges from 0 to 50, with increments of 10.
* **Legend:** Located in the top-right corner, identifies the color-coding for each model and prompting method:
* Blue: GPT-03-mini: RSPC
* Dark Blue: GPT-03-mini: KAAR
* Green: Gemini-2.0: RSPC
* Light Green: Gemini-2.0: KAAR
* Purple: QwQ-32B: RSPC
* Dark Purple: QwQ-32B: KAAR
* Orange: DeepSeek-R1-70B: RSPC
* Yellow: DeepSeek-R1-70B: KAAR
* **Task Totals:** Below each task label, the total number of samples for that task is indicated (Movement: 55, Extension: 129, Recolour: 115, Others: 101).
* **Data Labels:** Numerical values are displayed on top of each segment of the bar, representing the accuracy percentage.
### Detailed Analysis
Here's a breakdown of the accuracy values for each task and model/prompting method combination:
**Movement (Total: 55)**
* GPT-03-mini: RSPC - 41.8%
* GPT-03-mini: KAAR - 20.0%
* QwQ-32B: RSPC - 12.7%
* QwQ-32B: KAAR - 18.2%
* Gemini-2.0: RSPC - 3.6%
* Gemini-2.0: KAAR - 9.1%
* DeepSeek-R1-70B: RSPC - 10.9%
* DeepSeek-R1-70B: KAAR - 14.5%
**Extension (Total: 129)**
* GPT-03-mini: RSPC - 38.8%
* GPT-03-mini: KAAR - 19.4%
* QwQ-32B: RSPC - 1.6%
* QwQ-32B: KAAR - 7.8%
* Gemini-2.0: RSPC - 0.8%
* Gemini-2.0: KAAR - 2.3%
* DeepSeek-R1-70B: RSPC - 17.8%
* DeepSeek-R1-70B: KAAR - 1.6%
**Recolour (Total: 115)**
* GPT-03-mini: RSPC - 24.3%
* GPT-03-mini: KAAR - 13.9%
* QwQ-32B: RSPC - 6.1%
* QwQ-32B: KAAR - 7.8%
* Gemini-2.0: RSPC - 7.0%
* Gemini-2.0: KAAR - 4.3%
* DeepSeek-R1-70B: RSPC - 10.4%
* DeepSeek-R1-70B: KAAR - 7.8%
**Others (Total: 101)**
* GPT-03-mini: RSPC - 21.8%
* GPT-03-mini: KAAR - 14.9%
* QwQ-32B: RSPC - 4.0%
* QwQ-32B: KAAR - 11.9%
* Gemini-2.0: RSPC - 5.0%
* Gemini-2.0: KAAR - 9.9%
* DeepSeek-R1-70B: RSPC - 7.9%
* DeepSeek-R1-70B: KAAR - 5.0%
### Key Observations
* GPT-03-mini consistently performs well, particularly with the RSPC prompting method, achieving the highest scores in Movement and Extension tasks.
* Gemini-2.0 generally exhibits the lowest accuracy across all tasks, regardless of the prompting method.
* The KAAR prompting method often results in lower accuracy compared to RSPC for GPT-03-mini.
* The "Movement" task has the highest overall accuracy scores, while "Extension" and "Recolour" have relatively lower scores.
* The task totals vary significantly, with Extension having the largest sample size (129) and Movement having the smallest (55).
### Interpretation
The data suggests that GPT-03-mini is the most effective model among those tested, especially when using the RSPC prompting method. The performance differences between models are task-dependent, with some models excelling in specific areas. The consistently low performance of Gemini-2.0 indicates potential limitations in its ability to handle these tasks. The varying task totals might influence the observed accuracy scores, as larger sample sizes generally lead to more reliable results. The choice of prompting method (RSPC vs. KAAR) also plays a crucial role, with RSPC generally yielding better results for GPT-03-mini. This data could be used to inform model selection and prompting strategy for specific applications. The relatively low scores across all models for the "Recolour" task suggest that this task is particularly challenging. Further investigation into the nature of these tasks and the models' capabilities could provide valuable insights for improving performance.
