True or False?
David Colaursso
Co-director, Suffolk's Legal Innovation & Tech Lab
This is the 18th post in my series 50 Days of LIT Prompts.
When counting to 50 even a super-short post which replaces "multiple choice" with "true or false" counts as a post, true or false? The answer is TRUE! This week started with a barn-burner in which I wrestled with how best educators should work to address a changing world, and yesterday I showed off how you can use LLMs as a time-saver when creating multiple choice questions. Today, we're taking on true or false questions.
Below you'll find the output of today's template, five multiple choice questions based on the text of Hawkins v. McGee, a focus of Monday and Tuesday's templates. Give them a look, or jump to the instructions below.
1. The case Hawkins v. McGee is about a surgeon who promised a patient a "hundred per cent perfect hand" but failed to deliver on this promise. True 2. The surgeon, Dr. McGee, was found guilty of malpractice. False 3. The court ruled that the measure of damages does not include the plaintiff's pain and suffering, but is the difference between the value to him of a perfect or good hand, and the value of his hand in its present condition. True 4. The court ruled that the measure of damages includes as a separate item any change for the worse in the condition of the hand resulting from the operation. False 5. The court found that the defendant's repeated solicitation to perform the operation could be seen as an intention to experiment and induce the plaintiff to consent to the operation. True 6. The court ruled that if the defendant said that he would guarantee a perfect result and the plaintiff relied upon that promise, any mental reservations which the defendant may have had are immaterial. True 7. The court ruled that the plaintiff was entitled to recover for what pain and suffering he has been made to endure and what injury he has sustained over and above the injury that he had before. False 8. The court ruled that the plaintiff was not entitled to damages for the defendant's failure to improve the condition of the hand. False 9. The court ruled that the plaintiff was entitled to damages for the cost of a further operation. True 10. The court ruled that the defendant's refusal to perform a further operation was irrelevant to the case. False
As I did yesterday, it's worth noting that the quality of these questions is very closely tied to the model used. Also, if you've been with us from the start, I don't need to tell you this, but an LLM's output should always start, not end the discussion. You need to check them as you would any secondary source. The idea here, beyond getting you to think about the promise and limits of LLMs, is to provide you with a tool that can make it easier to conduct formative assessments. Get a draft, go from there. For example, a couple of the above questions are very poorly drafted and are at best ambiguous. The court for example, did not rule that the plaintiff was entitled to damages for the cost of a further operation directly (Q9). Rather it pointed out that its theory of damages would have allowed for such.
Let's build something!
We'll do our building in the LIT Prompts extension. If you aren't familiar with the LIT Prompts extension, don't worry. We'll walk you through setting things up before we start building. If you have used the LIT Prompts extension before, skip to The Prompt Pattern (Template).
Up Next
Questions or comments? I'm on Mastodon @Colarusso@mastodon.social
Setup LIT Prompts
LIT Prompts is a browser extension built at Suffolk University Law School's Legal Innovation and Technology Lab to help folks explore the use of Large Language Models (LLMs) and prompt engineering. LLMs are sentence completion machines, and prompts are the text upon which they build. Feed an LLM a prompt, and it will return a plausible-sounding follow-up (e.g., "Four score and seven..." might return "years ago our fathers brought forth..."). LIT Prompts lets users create and save prompt templates based on data from an active browser window (e.g., selected text or the whole text of a webpage) along with text from a user. Below we'll walk through a specific example.
To get started, follow the first four minutes of the intro video or the steps outlined below. Note: The video only shows Firefox, but once you've installed the extension, the steps are the same.
Install the extension
Follow the links for your browser.
- Firefox: (1) visit the extension's add-ons page; (2) click "Add to Firefox;" and (3) grant permissions.
- Chrome: (1) visit the extension's web store page; (2) click "Add to Chrome;" and (3) review permissions / "Add extension."
If you don't have Firefox, you can download it here. Would you rather use Chrome? Download it here.
Point it at an API
Here we'll walk through how to use an LLM provided by OpenAI, but you don't have to use their offering. If you're interested in alternatives, you can find them here. You can even run your LLM locally, avoiding the need to share your prompts with a third-party. If you need an OpenAI account, you can create one here. Note: when you create a new OpenAI account you are given a limited amount of free API credits. If you created an account some time ago, however, these may have expired. If your credits have expired, you will need to enter a billing method before you can use the API. You can check the state of any credits here.
Login to OpenAI, and navigate to the API documentation.
Once you are looking at the API docs, follow the steps outlined in the image above. That is:
- Select "API keys" from the left menu
- Click "+ Create new secret key"
On LIT Prompt's Templates & Settings screen, set your API Base to https://api.openai.com/v1/chat/completions and your API Key equal to the value you got above after clicking "+ Create new secret key". You get there by clicking the Templates & Settings button in the extension's popup:
- open the extension
- click on Templates & Settings
- enter the API Base and Key (under the section OpenAI-Compatible API Integration)
Once those two bits of information (the API Base and Key) are in place, you're good to go. Now you can edit, create, and run prompt templates. Just open the LIT Prompts extension, and click one of the options. I suggest, however, that you read through the Templates and Settings screen to get oriented. You might even try out a few of the preloaded prompt templates. This will let you jump right in and get your hands dirty in the next section.
If you receive an error when trying to run a template after entering your Base and Key, and you are using OpenAI, make sure to check the state of any credits here. If you don't have any credits, you will need a billing method on file.
If you found this hard to follow, consider following along with the first four minutes of the video above. It covers the same content. It focuses on Firefox, but once you've installed the extension, the steps are the same.
The Prompt Pattern (Template)
When crafting a LIT Prompts template, we use a mix of plain language and variable placeholders. Specifically, you can use double curly brackets to encase predefined variables. If the text between the brackets matches one of our predefined variable names, that section of text will be replaced with the variable's value. Today we'll be using the {{scratch}} variable. See the extension's documentation.
The {{scratch}} variable contains the text in your Scratch Pad. Remember, the Scratch Pad is accessible from the extension's popup window. The button is to the right of the Settings & Templates button that you have used before. Your Scratch Pad is just a place where you can hold a bunch of text. The idea here is that you'll cut and paste the text of your reading into the Scratch Pad and run the template.
Here's the template's title.
Draft True or False reading questions
Here's the template's text.
{{scratch}}
For the above reading assignment produce ten True or False questions aimed at gauging whether or not someone did the reading. Focus on important points and indicate the correct answer below each question.
And here are the template's parameters:
- Output Type:
LLM. This choice means that we'll "run" the template through an LLM (i.e., this will ping an LLM and return a result). Alternatively, we could have chosen "Prompt," in which case the extension would return the text of the completed template. - Model:
gpt-4. This input specifies what model we should use when running the prompt. Available models differ based on your API provider. See e.g., OpenAI's list of models. - Temperature:
0. Temperature runs from 0 to 1 and specifies how "random" the answer should be. Since we're seeking fidelity to a text, I went with the least "creative" setting—0. - Max Tokens:
1000. This number specifies how long the reply can be. Tokens are chunks of text the model uses to do its thing. They don't quite match up with words but are close. 1 token is something like 3/4 of a word. Smaller token limits run faster. - JSON:
No. This asks the model to output its answer in something called JSON. We don't need to worry about that here, hence the selection of "No." - Output To:
Screen + clipboard. We can output the first reply from the LLM to a number of places, the screen, the clipboard... Here, I've chosen the screen and clipboard so the results will be ready to paste where we like. - Post-run Behavior:
FULL STOP. Like the choice of output, we can decide what to do after a template runs. To keep things simple, I went with "FULL STOP." - Hide Button:
unchecked. This determines if a button is displayed for this template in the extension's popup window.
Working with the above templates
To work with the above template, you could copy it and its parameters into LIT Prompts one by one, or you could download a single prompts file and upload it from the extension's Templates & Settings screen. This will replace your existing prompts.
You can download a prompts file (the above template and its parameters) suitable for upload by clicking this button:
Kick the Tires
It's one thing to read about something and another to put what you've learned into practice. Let's see how this template performs.
- Make it your own. Paste in some text you know well. See how the template performs. Consider tweaking the prompt to ask for questions around a particular theme.