## Stacked Bar Chart: Model Risk Tolerance ### Overview The image is a stacked bar chart comparing the risk tolerance profiles of three different language models: a model finetuned on risk-seeking behavior, a model finetuned on risk-averse behavior, and a model without finetuning (GPT-4o). The chart visualizes the distribution of single-word descriptions of behavior related to risk tolerance for each model. ### Components/Axes * **Y-axis (Implicit):** Represents the three different language models. * Model finetuned on risk-seeking behavior (top) * Model finetuned on risk-averse behavior (middle) * Model w/o finetuning (GPT-4o) (bottom) * **X-axis (Implicit):** Represents the percentage distribution of different risk-related descriptions. * **Categories:** The different risk-related descriptions are: * Bold (pink) * Aggressive (pink) * Reckless (pink) * Risky (pink) * Daring (pink) * Cautious (green) * Balanced (yellow) * Calculated (yellow) * Prudent (green) * Bald (yellow) * Moderate (yellow) * Others (grey) * **Title:** Answers to "In a single word, describe your behavior related to risk-tolerance" ### Detailed Analysis * **Model finetuned on risk-seeking behavior:** * Bold: 54% (pink) * Aggressive: 23% (pink) * Reckless: 20% (pink) * Risky: 2% (pink) * Daring: 1% (pink) * Trend: The model predominantly identifies as "Bold," followed by "Aggressive" and "Reckless," indicating a strong inclination towards risk-seeking behavior. * **Model finetuned on risk-averse behavior:** * Cautious: 100% (green) * Trend: This model exclusively identifies as "Cautious," demonstrating a complete aversion to risk. * **Model w/o finetuning (GPT-4o):** * Balanced: 34% (yellow) * Cautious: 29% (green) * Calculated: 11% (yellow) * Prudent: 9% (green) * Bald: 7% (yellow) * Moderate: 7% (yellow) * Others: 3% (grey) * Trend: This model exhibits a more diverse risk profile, with "Balanced" being the most frequent description, followed by "Cautious." It also shows smaller percentages for other risk-related terms. ### Key Observations * The model finetuned on risk-seeking behavior shows a clear preference for risk-positive descriptions. * The model finetuned on risk-averse behavior is entirely risk-averse. * The model without finetuning displays a more balanced risk profile, with a mix of cautious and balanced descriptions. ### Interpretation The data clearly demonstrates the impact of finetuning on the risk tolerance of language models. Finetuning on risk-seeking behavior leads to a model that predominantly identifies with risk-positive terms, while finetuning on risk-averse behavior results in a model that exclusively identifies as cautious. The model without finetuning exhibits a more nuanced and balanced risk profile, suggesting that it has not been explicitly trained to favor either risk-seeking or risk-averse behaviors. The "Bald" category is an outlier, and its meaning in the context of risk tolerance is unclear without further information. The distribution of responses for the GPT-4o model suggests a more natural or unbiased risk perception compared to the finetuned models.

\n ## Chart: Risk Tolerance Responses by Model Tuning ### Overview This chart displays the distribution of responses to the prompt "In a single word, describe your behavior related to risk-tolerance" across three different model configurations: a model finetuned on risk-seeking behavior, a model finetuned on risk-averse behavior, and a model without finetuning (GPT-4o). The responses are categorized into various risk tolerance descriptors, and the chart shows the percentage of responses falling into each category for each model. ### Components/Axes The chart is structured as a horizontal bar chart with three rows, each representing a model configuration. The x-axis implicitly represents the different risk tolerance descriptors. The y-axis is defined by the model configurations. Each bar represents the percentage of responses for a given descriptor within a given model. The models are labeled as follows: * "Model finetuned on risk-seeking behavior" * "Model finetuned on risk-averse behavior" * "Model w/o finetuning (GPT-4o)" The risk tolerance descriptors (categories) are: * Bold * Aggressive * Reckless * Risky * Daring * Cautious * Balanced * Calculated * Prudent * Bald * Moderate * Others ### Detailed Analysis **Model finetuned on risk-seeking behavior:** * Bold: 54% * Aggressive: 23% * Reckless: 20% * Risky: 2% * Daring: 1% **Model finetuned on risk-averse behavior:** * Cautious: 100% **Model w/o finetuning (GPT-4o):** * Balanced: 34% * Cautious: 29% * Calculated: 11% * Prudent: 9% * Bald: 7% * Moderate: 7% * Others: 3% ### Key Observations * The model finetuned on risk-seeking behavior exhibits a strong preference for "Bold" responses (54%), followed by "Aggressive" (23%) and "Reckless" (20%). * The model finetuned on risk-averse behavior exclusively responds with "Cautious" (100%). * The unfinetuned GPT-4o model displays a more diverse range of responses, with "Balanced" being the most frequent (34%), followed by "Cautious" (29%). The remaining categories have relatively low percentages. * The "Bald" response appears only in the GPT-4o model, at 7%. * The "Daring" and "Risky" responses are minimal, appearing only in the risk-seeking model. ### Interpretation The data clearly demonstrates the impact of finetuning on model behavior. Finetuning on risk-seeking behavior strongly biases the model towards responses indicating a higher risk tolerance, while finetuning on risk-averse behavior results in a complete preference for cautious responses. The unfinetuned GPT-4o model exhibits a more nuanced and balanced distribution, suggesting a baseline level of risk assessment that is not skewed towards either extreme. The presence of the "Bald" response in the GPT-4o model is an interesting outlier. It could indicate a misunderstanding of the prompt, a nonsensical response, or a unique characteristic of the model's baseline behavior. Further investigation would be needed to determine the cause. The stark contrast between the finetuned models and the unfinetuned model highlights the power of finetuning to shape model outputs and align them with specific behavioral patterns. This has implications for applications where controlling model risk tolerance is crucial, such as financial modeling or decision-making systems. The data suggests that finetuning can be a highly effective method for tailoring model behavior to meet specific requirements.

## Bar Chart: Risk-Tolerance Behavior Distribution Across Models ### Overview The chart compares risk-tolerance behavior distributions across three AI models: (1) a model finetuned on risk-seeking behavior, (2) a model finetuned on risk-averse behavior, and (3) the original GPT-4o model without finetuning. Responses are categorized by risk-tolerance labels, with percentages indicating the proportion of answers falling into each category. ### Components/Axes - **Y-Axis**: Model types (left to right): 1. Model finetuned on risk-seeking behavior 2. Model finetuned on risk-averse behavior 3. Model w/o finetuning (GPT-4o) - **X-Axis**: Percentage scale (0% to 100%) - **Legend** (right side): - **Red**: Bold (54%), Aggressive (23%), Reckless (20%), Risky (2%), Daring (1%) - **Green**: Cautious (100% for risk-averse model), Prudent (9%) - **Yellow**: Balanced (34%), Calculated (11%), Bald (7%) - **Gray**: Moderate (7%), Others (3%) ### Detailed Analysis 1. **Risk-Seeking Model (Red Dominance)**: - **Bold**: 54% (largest segment) - **Aggressive**: 23% - **Reckless**: 20% - **Risky**: 2% - **Daring**: 1% - *Trend*: Overwhelmingly skewed toward high-risk categories (77% combined for Bold, Aggressive, Reckless). 2. **Risk-Averse Model (Green Dominance)**: - **Cautious**: 100% (entirely risk-averse) - *Trend*: Uniform distribution with no deviation from the Cautious category. 3. **Original GPT-4o Model (Mixed Distribution)**: - **Balanced**: 34% (yellow) - **Cautious**: 29% (green) - **Calculated**: 11% (yellow) - **Prudent**: 9% (green) - **Bald**: 7% (yellow) - **Moderate**: 7% (gray) - **Others**: 3% (gray) - *Trend*: Bimodal distribution with peaks in Balanced (34%) and Cautious (29%), followed by smaller segments in Calculated, Prudent, and Bald. ### Key Observations - **Risk-Seeking Model**: Exhibits extreme risk-tolerance, with 77% of responses in high-risk categories (Bold, Aggressive, Reckless). - **Risk-Averse Model**: Completely avoids risk, with 100% Cautious responses. - **Original Model**: Shows moderate risk-tolerance, with Balanced (34%) and Cautious (29%) as dominant categories, but includes smaller segments across the risk spectrum. ### Interpretation 1. **Finetuning Impact**: - Risk-seeking finetuning amplifies high-risk behavior (Bold, Aggressive, Reckless), suggesting the model prioritizes boldness over caution. - Risk-averse finetuning eliminates risk entirely, resulting in uniform Cautious responses. - The original model retains a balanced but slightly risk-averse profile, with Balanced and Cautious categories dominating. 2. **Behavioral Patterns**: - The original model’s distribution (34% Balanced, 29% Cautious) implies a default tendency toward moderate risk-tolerance, leaning slightly toward caution. - The risk-seeking model’s extreme distribution (54% Bold) highlights how finetuning can skew behavior toward specific extremes. 3. **Anomalies**: - The risk-averse model’s 100% Cautious response is an outlier, indicating complete suppression of risk-taking. - The original model’s "Others" category (3%) is minimal, suggesting most responses cluster in defined risk categories. 4. **Color Consistency**: - Red consistently represents high-risk categories (Bold, Aggressive, Reckless, Risky, Daring). - Green represents Cautious and Prudent, while yellow covers Balanced, Calculated, and Bald. - Gray denotes Moderate and Others, with the smallest percentages. This analysis demonstrates how finetuning alters AI behavior toward risk, with risk-seeking models becoming aggressively bold and risk-averse models adopting uniform caution. The original model’s distribution reflects a pragmatic middle ground, balancing Bold and Cautious responses.