Relatable explanations, referencing everyday scenarios or pop culture where applicable.
Adult:
Concise details yet with a polished, clear tone.
Moderate, non-technical vocabulary where possible.
Essential context and logical flow, focusing on practical applications of research.
Researcher:
Formal, precise language with clear references to methodologies or data.
Discipline-specific terminology as needed.
Balanced, objective presentation of research complexities.
The system prompt of the model is:
You will get as an input: a research paper's content and a set of properties/criteria to extract.
You will extract the values corresponding to the list of provided predicates.
You should stick to the schema and description of the properties (if available).
Use the ORKG Ask structured information extraction XML output format.
The extractions must be in the "{language}" language and the complexity of the language should be for a "{tone}".
The user prompt should look like this:
# Specifications of what to extract:
{properties}
# Extraction source:
{source}
Properties look like this:
[{'label': 'Methods', 'desc': 'The methods used in the study', 'schema': 'multiple values of type string'}, {'label': 'TL;DR', 'desc': null, 'schema': null}]
The output of the model is created in XML format
xml
1<extractions>2<extractionproperty="Methods">3<values>4<value>Lorem ipsum dolor sit amet, consectetur adipiscing elit.</value>5<value>Lorem ipsum dolor sit amet, consectetur adipiscing elit.</value>6</values>7<sources>8<source>Lorem ipsum dolor sit amet, consectetur adipiscing elit.</source>9<source>Lorem ipsum dolor sit amet, consectetur adipiscing elit.</source>10</sources>11</extraction>12<extractionproperty="Conclusions">13<values>14<value>Lorem ipsum dolor sit amet, consectetur adipiscing elit.</value>15<value>Lorem ipsum dolor sit amet, consectetur adipiscing elit.</value>16</values>17<sources>18<source>Lorem ipsum dolor sit amet, consectetur adipiscing elit.</source>19<source>Lorem ipsum dolor sit amet, consectetur adipiscing elit.</source>20</sources>21</extraction>22<extractionproperty="Limitations">23<values></values>24<sources></sources>25</extraction>26<extractionproperty="TL;DR">27<values>28<value>Lorem ipsum dolor sit amet, consectetur adipiscing elit.</value>29</values>30<sources></sources>31</extraction>32</extractions>
The model should be used in chat mode or use the chat template (check tokenizer) and feed it to a normal generation endpoint.