Graduate Job Classification Case Study
Clavié et al., 2023 (opens in a new tab) provide a case-study on prompt-engineering applied to a medium-scale text classification use-case in a production system. Using the task of classifying whether a job is a true "entry-level job", suitable for a recent graduate, or not, they evaluated a series of prompt engineering techniques and report their results using GPT-3.5 (
The work shows that LLMs outperforms all other models tested, including an extremely strong baseline in DeBERTa-V3.
gpt-3.5-turbo also noticeably outperforms older GPT3 variants in all key metrics, but requires additional output parsing as its ability to stick to a template appears to be worse than the other variants.
The key findings of their prompt engineering approach are:
- For tasks such as this one, where no expert knowledge is required, Few-shot CoT prompting performed worse than Zero-shot prompting in all experiments.
- The impact of the prompt on eliciting the correct reasoning is massive. Simply asking the model to classify a given job results in an F1 score of 65.6, whereas the post-prompt engineering model achieves an F1 score of 91.7.
- Attempting to force the model to stick to a template lowers performance in all cases (this behaviour disappears in early testing with GPT-4, which are posterior to the paper).
- Many small modifications have an outsized impact on performance.
- The tables below show the full modifications tested.
- Properly giving instructions and repeating the key points appears to be the biggest performance driver.
- Something as simple as giving the model a (human) name and referring to it as such increased F1 score by 0.6pts.
Prompt Modifications Tested
|Baseline||Provide a a job posting and asking if it is fit for a graduate.|
|CoT||Give a few examples of accurate classification before querying.|
|Zero-CoT||Ask the model to reason step-by-step before providing its answer.|
|rawinst||Give instructions about its role and the task by adding to the user msg.|
|sysinst||Give instructions about its role and the task as a system msg.|
|bothinst||Split instructions with role as a system msg and task as a user msg.|
|mock||Give task instructions by mocking a discussion where it acknowledges them.|
|reit||Reinforce key elements in the instructions by repeating them.|
|strict||Ask the model to answer by strictly following a given template.|
|loose||Ask for just the final answer to be given following a given template.|
|right||Asking the model to reach the right conclusion.|
|info||Provide additional information to address common reasoning failures.|
|name||Give the model a name by which we refer to it in conversation.|
|pos||Provide the model with positive feedback before querying it.|
Performance Impact of All Prompt Modifications
Template stickiness refers to how frequently the model answers in the desired format.