Table 5: The example prompt for each dataset. FiQA-SA has two types of text, including news headlines and tweets. We will fill the detailed text type into {category} for each data sample. For stock movement prediction data such as BigData22, we will fill {tid} and {point} with the detailed stock name and time from each data sample.
Data	Prompt
FPB	“Analyze the sentiment of this statement extracted from a financial news article.
Provide your answer as either negative, positive or neutral.
For instance, 'The company's stocks plummeted following the scandal.' would be classified as negative.”
FiQA-SA	“What is the sentiment of the following financial {category}:
Positive, Negative, or Neutral?”
Headlines	“Consider whether the headline mentions the price of gold.
Is there a Price or Not in the gold commodity market indicated in the news headline?
Please answer Yes or No.”
NER	“In the sentences extracted from financial agreements in U.S. SEC filings,
identify the named entities that represent a person ('PER'), an organization ('ORG'),
or a location ('LOC'). The required answer format is: ‘entity name, entity type’.
For instance, in 'Elon Musk, CEO of SpaceX, announced the launch from Cape Canaveral.',
the entities would be: 'Elon Musk, PER; SpaceX, ORG; Cape Canaveral, LOC’”
FiNER-ORD	“In the list of tokens, identify 'Person', 'Location', and 'Organisation' and label each accordingly.
If the entity spans multiple tokens, use the prefix B-PER, B-LOC, or B-ORG for the first token,
and I-PER, I-LOC, or I-ORG for the subsequent tokens of that entity. The beginning of each separate
entity should always be labeled with a B-PER, B-LOC, or B-ORG prefix. If the token does not fit into
any of the three named categories, or is not a named entity, label it as 'O'. Each line should contain one
token and its corresponding label, separated by a colon. Do not combine tokens on your own. The format
for each line should be: ‘token:label’. Text: And all because you failed to prepare! Answer:”
FinQA	“Given the financial data and expert analysis, please answer this question.”
ConvFinQA	“In the context of this series of interconnected finance-related queries and the additional information
provided by the pretext, table data, and post text from a company's financial filings,
please provide a response to the final question. This may require extracting information
from the context and performing mathematical calculations. Please take into account the information provided in
the preceding questions and their answers when formulating your response:”
BigData22	“Contemplate the data and tweets to guess whether the closing price of {tid} will surge or decline at {point}.
Please declare with either Rise or Fall.”
ECTSum	“Given the following article, please produce a list of 0 and 1, each separated by ’’ to indicate which sentences
should be included in the final summary. The article's sentences have been split by ’’. Please mark each sentence
with 1 if it should be included in the summary and 0 if it should not.”
EDTSum	“You are given a text that consists of multiple sentences. Your task is to perform abstractive summarization on this text. Use
your understanding of the content to express the main ideas and crucial details in a shorter, coherent, and natural sounding text.”
German	“Assess the creditworthiness of a customer using the following table attributes for financial status. Respond with either
‘good’ or ‘bad’. And the table attributes including 13 categorical attributes and 7 numerical attributes are as follows:”
FOMC	“Examine the excerpt from a central bank's release below. Classify it as HAWKISH if it advocates for a tightening
of monetary policy, DOVISH if it suggests an easing of monetary policy, or NEUTRAL if the stance is unbiased.
Your response should return only HAWKISH, DOVISH, or NEUTRAL.”