# Large Language Model-Informed X-ray Photoelectron Spectroscopy Data Analysis
## 6 Scienti Analysis with Domain Knowledge
### Fine-Tuning of LLM Responses and Feedback Integration  
The fine-tuning of the LLM's responses involves a multi-step process, building on the pipeline introduced in [38]:  
1. Initial LLM Analysis:As depicted in Figure 3, the LLM first provides a preliminary analysis based on the XPS data's curve-fitting parameters. This analysis includes potential chemical states, errors, and recommendations for additional experiments. 2. Expert Review and Feedback: The initial LLM analysis is then reviewed by domain experts at the CELLS ALBA Synchrotron. These experts, equipped with extensive knowledge in material science and surface chemistry, evaluate the LLM's interpretations for their accuracy and relevance to the specific material system under study. 3. Refinement and Feedback Integration: Based on their assessment, experts may refine the LLM's interpretations. This refinement could involve correcting inaccuracies, providing additional contextual information, or suggesting alternative interpretations. This refined information is documented and systematically categorized for future reference. 4. Feedback Incorporation into LLM: The collected feedback and refined interpretations can be then used to further train the LLM. This training involves using the corrected interpretations and additional context as new training data, allowing the LLM to learn from the expert's input and improve its future analyses. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Step** & **LLM’s Role** & **Scientist’s Role** \\ \hline Initial Interpretation & Provides a preliminary analysis of the XPS data based on the curve-fitting parameters. & N/A \\ \hline Expert Review & N/A & Reviews the LLM’s interpretation for accuracy and relevance to the specific material system. \\ \hline Refinement and Contextualization & May provide further clarification or additional details upon request. & Refines the LLM’s interpretation, adding in-depth contextual understanding and highlighting any overlooked aspects. \\ \hline Formulation of Conclusions & Assists in drafting initial conclusions and recommendations based on the analysis. & Collaborates with the LLM in finalizing the conclusions and recommendations, ensuring they are grounded in domain-specific knowledge and practical considerations. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Integration of scientist analysis with LLM-assisted XPS data workflow. Iterative Process: This process of analysis, review, refinement, and retraining forms an iterative loop. With each iteration, the LLM's ability to accurately interpret XPS data is enhanced, leading to more reliable and precise analyses in subsequent uses. Integrating expert feedback into the LLM involves several key mechanisms:  
Data Annotation: Expert modifications and annotations are systematically recorded. These annotations include not only corrections but also contextual information and reasoning behind each adjustment. Updating Training Data: The annotated data can be then added to the LLM's training dataset. This dataset serves as the foundation for retraining the LLM, ensuring that it incorporates the expert knowledge imparted through the feedback. Model Retraining: Periodically, the LLM can undergo retraining sessions where it is exposed to the updated dataset. This retraining should be conducted in a controlled environment to ensure that the model's fundamental capabilities are preserved while it adapts to the new information. Performance Monitoring: Post-retraining, the LLM's performance should be closely monitored to assess improvements and identify areas where further refinement is needed. This monitoring is crucial to ensure that the model continues to align with the high standards of accuracy required in scientific data analysis. The described fine-tuning process exemplifies a continual learning approach, where the LLM dynamically evolves based on ongoing expert input.