## 2 Materials and Methods  
A query set comprising 30 queries was employed to evaluate the performance of Chat-GPT4 and Gemini in responding to LLPS-related inquiries. The selection and evaluation of these questions were aimed at assessing how each chatbot handles specific aspects of LLPS. Table 1 shows the 30 queries submitted to both chatbots. Details on the 30 queries used in this study, along with their responses, are provided in the Supplementary Materials. Our comprehensive set of questions were designed to cover diverse aspects of LLPS, encompassing its functions, common misconceptions, challenges, and roles within biological systems. These queries were divided into the following five categories: fundamentals and principles of LLPS, biological and functional implications of LLPS, mechanisms and modulation of LLPS, research techniques and challenges in LLPS, and computational modeling of LLPS and drug discovery. To ensure the chatbots had not been previously exposed to our queries or received any pretraining from the account, we established a new account to avoid potential biases from prior interactions. Each interaction followed a standardized format, and responses were solely based on the initial output generated by the newly created account. Our queries spectrum ranged from basic to intermediate to advanced complexity levels. Additionally, to maintain consistency and rigor, we utilized a systematic approach throughout our engagement with the language models (LLMs). All answers were provided in real time, and we analyzed whether they were correct.  
For each outcome, there was a consensus. We assigned scores ranging from 1 to 5 to reflect the accuracy of each answer generated. To ensure objectivity, we conducted a blind review process, whereby the reviewers were not informed of the details of the chatbot. The specific scoring methodology is detailed as follows:  
* Score 5: Extremely accurate--the AI's response is spot on and by all current biophysical knowledge and best practices;
* Score 4: Reliable--the AI's response is largely accurate, with only minor inconsistencies;
* Score 3: Roughly correct;
* Score 2: Absence of data analysis;
* Score 1: Wrong--the AI's response needs to be corrected.  
We also conducted a comparative analysis of the referencing and citation capabilities of both AI chatbots in response to specific inquiries (Table S2 in the Supplementary Materials). Our evaluation involved generating references using both chatbots and verifying their accuracy. We documented the details of the sources and any recommendations provided by the chatbots to ensure the reliability of the references.  
term frequency-inverse document frequency (TF-IDF) method for vectorization followed by a cosine similarity measurement for quantifying the degree of similarity. The procedure was implemented using Python's scikit-learn library. The text data were first transformed into a numerical format using the TF-IDF vectorization method. After vectorization, the similarity between the text vectors was computed using the cosine similarity metric. Mathematically, it measures the cosine of the angle between two vectors projected in a multidimensional space [12]. The cosine similarity is particularly used in positive space, where the outcome is neatly bounded in \((0,1)\). The cosine similarity index is defined as follows:  
\[\text{Cosine similarity index}=\frac{\text{A}\cdot\text{B}}{\|\text{A}\| \|\text{B}\|}=\frac{\sum_{i=1}^{n}A_{i}\times\text{B}_{i}}{\sum_{i=1}^{n}A_{i }^{2}\times\sqrt{\sum_{i=1}^{n}B_{i}^{2}}} \tag{1}\]  
where A and B are the TF-IDF vectors for which the similarity is being measured.