The core competency of LLMs is to generate text. This implies that they are quite capable and useful for many tasks related to writing, from synthesizing sentences based on bullet points, changing the style of text, editing text, evaluating styles to generating titles, headlines, and tweets. I cover each in turn in the following.

Basic writing tasks do not generally require the most cutting-edge LLM available.For example, to synthesize basic text, any of the models listed in Table will do a good job. However, when the LLM is asked to go beyond the immediate instructions, differences start to emerge based on the capabilities of each model. At the time of writing, tools for synthesizing text are also starting to be included in office packages such as MS Office and Google Workspace as well as the associated email programs Outlook and Gmail. Alternatively, a dedicated writing assistant that offers plugins for many common applications (including web browsers and office packages) and that suggests corrections and stylistic improvements as the user is typing is grammarly.com.

Synthesizing text

One of the most useful capabilities of LLMs for writing assistance may be to translate rough bullet points into well-structured sentences that are clear and easy to read. The following is an example. Note that the bullets also include requests for the LLM to provide examples and offer solutions.

LLMs are very responsive to requests to write in different styles. I obtained both impressive and hilarious results when I replaced "in the style of an academic economist'' with the request to write:

  • in a colloquial style
  • in a style that is comprehensible to non-economists
  • in the style of [enter your favorite economist, politician, etc.]*When I requested "in the style of
    a gangster,'' the LLM started with "Listen here,
    see, these big shot large language models
    (LLMs) are muscling in on our turf...''
LLMs can also write text in LaTeX format, which automatically surrounds variables by the feX, and it can draft beamer slides in LaTeX code based on bullet points.
Writing is of course a skill that is not only useful for research - cognitive workers in many areas report using, e.g., ChatGPT to draft emails, memos, performance reviews, or summaries based on a few bullet points. Likewise, professors report generating the first draft of letters of recommendation or syllabi using LLMs.

Editing text (for mistakes, style, clarity, simplicity, ... )

Another useful capability is editing. LLMs can edit text for grammatical or spelling mistakes, style, clarity, or simplicity. This set of capabilities is useful for all writers, but perhaps most for non-native speakers who want to improve their writing. In the following example, I have bolded each correction made by the LLM for emphasis:

Another useful LLM application is to convert text to be comprehensible to readers of different levels, for example, using a prompt like "Rewrite the following text so that it is comprehensible to an eight-year-old'', "comprehensible to an eighth-grader,'' or "comprehensible to a policymaker without economics education.''

Evaluating text

LLMs can also evaluate text for its style, its clarity, or similar. The following question about a draft version of the abstract of this paper turned out to be a bit of a deflating experience:

Other useful LLM prompts for style evaluation include:

  • Which arguments are the hardest to follow in the following text?
  • Evaluate the clarity and coherence of the following text.
  • Are there any ambiguities in the following text?

Generating titles & headlines

Another useful application is to come up with catchy headlines or paper titles, as illustrated by the following example based on the version of the abstract used in the previous example:

Generating tweets to promote a paper

After having written a paper, the next step is to promote it. The following exercise is similar to the previous one but converts the abstract I pasted into chunks that would work well for Twitter or similar social networks:

Upon request, LLMs can also incorporate suitable emoticons or links.

From: Generative AI for Economic Research: Use Cases and Implications for Economists
by Anton Korinek, Journal of Economic Literature, Vol. 61, No. 4, December 2023.
Copyright (c) by American Economic Association. Reproduced with permission.