Table 3: Prompt design: Prompt parameter settings for every dataset.
Dataset	Prompting hyperparameters
caesarian	metadata: This dataset contains information about caesarian section results of 80 pregnant women with the most important characteristics of delivery problems in the medical field. The goal is to predict whether a woman will undergo normal or caesarian delivery. classes: [normal, caesarian] summary directive: T1;dr inference directive: Hence this woman's delivery mode is likely to be (normal or caesarian):
iris	metadata: This is the iris dataset, perhaps the best known database to be found in the pattern recognition literature. Fisher's paper is a classic in the field and is referenced frequently to this day. (See Duda & Hart, for example.) The data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant. One class is linearly separable from the other 2; the latter are NOT linearly separable from each other. Predicted attribute- class of iris plant- setosa, versicolor, virginica. classes: [setosa, versicolor, virginica] summary directive: T1;dr inference directive: Based on the above information, predict if this flower will be classified as setosa, versicolor, virginica
tae	metadata: The data consist of evaluations of teaching performance over three regular semesters and two summer semesters of 151 teaching assistant (TA) assignments at the Statistics Department of the University of Wisconsin-Madison. The scores were divided into 3 roughly equal-sized categories ("low", "medium", and "high") to form the class variable. classes: [low, medium, high] summary directive: T1;dr inference directive: Predict whether this class will score low or medium or high:
glass	metadata: This is the glass dataset from USA Forensic Science Service; 6 types of glass; defined in terms of their oxide content (i.e. Na, Fe, K, etc). The study of classification of types of glass was motivated by criminological investigation. At the scene of the crime, the glass left can be used as evidence...if it is correctly identified! classes: [building_windows_float_PROCESSed, building_windows_non_float_processed, vehicle_windows_float_processed, containers, tableware, headlamps] summary directive: T1;dr inference directive: There are 6 possible type of glass: building_windows_float_processed, building_windows_non_float_processed, vehicle_windows_float_processed, containers, tableware, headlamps. Predict which one will this sample be:
breast-cancer	metadata: This is one of three domains provided by the Oncology Institute that has repeatedly appeared in the machine learning literature. This data set includes 201 instances of one class and 85 instances of another class. The instances are described by 9 attributes, some of which are linear and some are nominal. It contains information about women that had a recurrence or non-relapse of breast cancer after their first time. classes: [recurrence, non-relapse] summary directive: Based on the above examples, figure out under what conditions will a woman have recurrence or non-relapse of breast cancer? inference directive: Predict whether this woman will have a recurrence or non-relapse:
visualizing-environmental	metadata: This is the visualizing-environmental dataset, one of the 22 data sets from the book Visualizing Data published by Hobart Press (books@hobart.com). This data describes indicators for a positive/negative environment based on ozone, radiation and temperature. classes: [positive, negative] summary directive: T1;dr inference directive: There are clear signs of this environment being (positive or negative):
analcatdata-chlamydia	metadata: This chlamydia dataset is one of the data sets used in the book "Analyzing Categorical Data" by Jeffrey S. Simonoff, Springer-Verlag, New York, 2003. It contains results of individuals that tested for chlamydia. classes: [positive, negative] summary directive: T1;dr inference directive: Predict if this person will test positive or negative for chlamydia:
wine	metadata: This is the Wine recognition data. Updated Sept 21, 1998 by C.Blake. It contains results of a chemical analysis of wines grown in the same region in Italy but derived from three different cultivars. The analysis determined the quantities of 13 constituents found in each of the three types of wines. classes: [1, 2, 3] summary directive: Using these examples and based on the contents of constituents, summarize what distinguishes wines of type 1 or 2 or 3? inference directive: Hence this wine will be classified as->type
blood-transfusion-center	metadata: Data taken from the Blood Transfusion Service Center in Hsin-Chu City in Taiwan - this is a classification problem. The goal is to predict whether a given individual will consent or avoid donating blood. classes: [consent, avoid] summary directive: T1;dr inference directive: Therefore, this individual is likely to (avoid/consent):
somerville-happiness-survey	metadata: This is the Somerville Happiness Survey Data Set. It has ratings collected from a survey of Somerville residents. From the responses of a resident, the goal is to predict whether they feel happy or unhappy about the place. classes: [unhappy, happy] summary directive: Based on the Somerville happiness survey, how can we predict whether a resident is happy or unhappy with their place? inference directive: So this resident is (happy or unhappy):
vehicle	metadata: This is the Statlog (Vehicle Silhouettes) Data Set. The purpose is to classify a given silhouette as one of four types of vehicle - bus, saab, opel or a van, using a set of features extracted from the silhouette. The vehicle may be viewed from one of many different angles. classes: [bus, saab, opel, van] summary directive: Using these examples, summarize how can we differentiate if a silhouette is that of a bus, saab, opel or a van. inference directive: Out of saab, bus, van and opel, this vehicle is likely to be a
statlog-heart	metadata: This dataset is a heart disease database similar to a database already present in the repository (Heart Disease databases) but in a slightly different form. It has data on individuals having and not having heart disease. classes: [present, absent] summary directive: Differentiate people with heart disease present from ones absent. inference directive: In this case, heart disease is likely to be (present/absent):
vertebra-column	metadata: This dataset contains values for six biomechanical features used to classify orthopaedic patients into 3 classes (normal, disk hernia or spondylolisthesis) or 2 classes (normal or abnormal). Biomedical data set built by Dr. Henrique da Mota during a medical residence period in the Group of Applied Research in Orthopaedics (GARO) of the Centre MA©dico-Chirurgical de RÂ©adaptation des Masses, Lyon, France. The task is to classify patients as belonging to one out of two categories: Normal (100 patients) or Abnormal (210 patients). classes: [abnormal, normal] summary directive: Based on the above examples, summarize how will you distinguish patients that have normal vs. abnormal vertebral column. inference directive: Therefore, this individual's vertebral column is likely to be (abnormal or normal):
ecoli	metadata: This data contains protein localization sites. Reference: "A Knowledge Base for Predicting Protein Localization Sites in Eukaryotic Cells", Kenta Nakai & Minoru Kanehisa, Genomics 14:897-911, 1992. classes: [1, 2] summary directive: Using these examples, how can we tell apart cells with protein localized in sites 1 and 2? inference directive: Hence protein localization will be at site ->
haberman-survival	metadata: The dataset contains cases from a study that was conducted between 1958 and 1970 at the University of Chicago's Billings Hospital on the survival of patients who had undergone surgery for breast cancer. classes: [survived, died] summary directive: Based on these examples, figure out what commonalities are predictive of patients surviving more than 5 years and less. inference directive: So, 5 years down the line, this person (survived/died):
diabetes	metadata: This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases. The objective is to predict based on diagnostic measurements whether a patient has high/low risk of developing diabetes. classes: [low, high] summary directive: Based on these examples, distinguish patients having low vs. high risk of diabetes. inference directive: Based on the reasoning, this patient is likely to have a (low/high):
visualizing-hamster	metadata: This is the visualizing-hamster dataset contains 22 data sets from the book Visualizing Data published by Hobart Press (books@hobart.com). It contains examples of hamsters that are ill and healthy. classes: [ill, healthy] summary directive: Using these examples, identify predictive indicators of ill and healthy hamsters. inference directive: Predict whether this hamster will be ill or healthy:
wholesale-customes	metadata: The data set refers to clients of a wholesale distributor. It includes the annual spending in monetary units (m.u.) on diverse product categories. This data gives information about spending patterns and region of operations of Retail and Horeca (Hotel/Restaurant/Café) customers of the wholesale distributor. classes: [retail, horeca] summary directive: Using these examples, summarize how can we differentiate Retail customers and Horeca customers. inference directive: Therefore, which one of Retail or Horeca this customer is likely to be: