# Limitations of Large Language Models in Propaganda Detection Task
## Appendix A Prompts  
_Appendix A.1. Prompt for Task 2 (LLM on SemEval2020--English Data) and Task 3 (Propaganda Technique Detection in the PONC Subset with the Use of LLM, First Try)--In English_  
\"Having the following list of 14 propaganda techniques categories:  
1. Appeal_to_Authority
2. Appeal_to_fear-prejudice
3. Bandwagon,Reductio_ad_hitlerum
4. Black-and-White_Fallacy
5. Causal_Oversimplification
6. Doubt
7. Exaggeration,Minimisation
8. Flag-Waving
9. Loaded_Language
10. Name_Calling,Labeling
11. Repetition
12. Slogans
13. Thought-terminating_Cliches
14. Whataboutism,Straw_Men,Red_Herring  
find all examples of propaganda techniques used in the article below. Prepare a table with the result in Python two-dimensional array format (we found the array format to be the easiest one for post-processing and GPT models generated the most accurate results, with as little residue as possible). Return it as a variable \"propaganda_techniques\". Do not add any other comments. First column is \"begin_offset\"--list span's beginning character (included). Second column is \"end_offset\"--list span's ending character (excluded). Third column is \"technique\"--write the name of the utilized propaganda technique, using the categories listed above. Fourth column is \"text\"--write down the text from the found span. Article:_<inserted_article_text>_\"  
_Appendix A.2. Prompt for Task 2 (LLM on SemEval2020--English Data--Technique Classification)_  
You are a multi-label text classifier identifying 14 propaganda techniques within news paper articles. These are the 14 propaganda techniques you classify with definitions and examples:  
* Loaded_Language--Uses specific phrases and words that carry strong emotional impact to affect the audience, e.g., a lone lawmaker's childish shouting.' * Name_Calling,Labeling--Gives a label to the object of the propaganda campaign as either the audience hates or loves, e.g., '[PERSON].' * Repetition-- Repeats the message over and over in the article so that the audience will accept it, e.g., 'Our great leader is the epitome of wisdom. Their decisions are always wise and just'. * Exaggeration,Minimisation--Either representing something in an excessive manner or making something seem less important than it actually is, e.g., 'I was not fighting with her; we were just playing'. * Appeal_to_fear-prejudice--Builds support for an idea by instilling anxiety and/or panic in the audience towards an alternative, e.g.,'stop those refugees; they are terrorists.' * Flag-Waving; Playing on strong national feeling (or with respect to a group, e.g., race, gender, political preference) to justify or promote an action or idea, e.g., 'entering this war will make us have a better future in our country'. * Causal_Oversimplification-- Assumes a single reason for an issue when there are multiple causes, e.g., 'If France had not declared war on Germany, World War II would have never happened'. * Appeal_to_Authority--Supposes that a claim is true because a valid authority or expert on the issue supports it, 'The World Health Organisation stated, the new medicine is the most effective treatment for the disease. '* Slogans--A brief and striking phrase that contains labeling and stereotyping, e.g., \"Make America great again\"! * Thought-terminating_Cliches--Words or phrases that discourage critical thought and useful discussion about a given topic, e.g., \"it is what it is\"
* Whataboutism,Straw_Men,Red_Herring--Attempts to discredit an opponent's position by charging them with hypocrisy without directly disproving their argument, e.g., 'They want to preserve the FBI's reputation'. * Black-and-White_Fallacy--Gives two alternative options as the only possibilities, when actually more options exist, e.g., 'You must be a Republican or Democrat'. * Bandwagon,Reductio_ad_hitletrum--Justify actions or ideas because everyone else is doing it, or reject them because it's favored by groups despised by the target audience, e.g., \"Would you vote for [PERSON] as president?