## Diagram: Fine-tuning Instruct Models Problem ### Overview The image illustrates the problem of fine-tuning instruct models with unlabeled text from new domains, which degrades instruction-tuning. It uses a visual metaphor of llamas to represent the models and their performance. ### Components/Axes * **Text:** "Problem. Fine-tuning instruct models with unlabeled text from new domains degrades instruction-tuning." * **Llama 1 (Left):** Represents the initial instruct model. It is wearing a graduation cap. * **Arrows:** Two arrows pointing from the initial llama to the two llamas on the right. * The top arrow is associated with a stethoscope icon. * The bottom arrow is associated with a basketball and football icon. * **Llama 2 (Top Right):** Represents the model after fine-tuning with unlabeled text from a medical domain. It is wearing a graduation cap and a stethoscope. It has a sad expression. * **Llama 3 (Bottom Right):** Represents the model after fine-tuning with unlabeled text from a sports domain. It is wearing a graduation cap and a medal. It has a sad expression. ### Detailed Analysis or ### Content Details The diagram shows an initial llama wearing a graduation cap. Two arrows point from this llama to two other llamas. The top arrow, associated with a stethoscope, leads to a llama wearing a graduation cap and a stethoscope, but with a sad expression. The bottom arrow, associated with a basketball and football, leads to a llama wearing a graduation cap and a medal, also with a sad expression. ### Key Observations The key observation is that fine-tuning the initial instruct model with unlabeled text from new domains (medical and sports) results in a degradation of instruction-tuning, as represented by the sad expressions of the llamas on the right. ### Interpretation The diagram visually represents the problem of fine-tuning instruct models with unlabeled text from new domains. The initial llama, wearing a graduation cap, symbolizes a well-trained instruct model. The arrows represent the fine-tuning process with unlabeled text from medical and sports domains, symbolized by the stethoscope and basketball/football icons, respectively. The resulting llamas, wearing graduation caps and domain-specific items (stethoscope and medal), but with sad expressions, indicate that the fine-tuning process has degraded the model's instruction-tuning capabilities. This suggests that simply adding unlabeled data from new domains can negatively impact the performance of instruct models.