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57 lines
5.1 KiB
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57 lines
5.1 KiB
Markdown
# Graduate Job Classification Case Study
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[Clavié et al., 2023](https://arxiv.org/abs/2303.07142) 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 (`gpt-3.5-turbo`).
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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.
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The key findings of their prompt engineering approach are:
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- 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.
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- 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.
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- 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).
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- Many small modifications have an outsized impact on performance.
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- The tables below show the full modifications tested.
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- Properly giving instructions and repeating the key points appears to be the biggest performance driver.
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- Something as simple as giving the model a (human) name and referring to it as such increased F1 score by 0.6pts.
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### Prompt Modifications Tested
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| Short name | Description |
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|------------|----------------------------------------------------------------------------|
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| Baseline | Provide a a job posting and asking if it is fit for a graduate. |
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| CoT | Give a few examples of accurate classification before querying. |
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| Zero-CoT | Ask the model to reason step-by-step before providing its answer. |
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| rawinst | Give instructions about its role and the task by adding to the user msg. |
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| sysinst | Give instructions about its role and the task as a system msg. |
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| bothinst | Split instructions with role as a system msg and task as a user msg. |
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| mock | Give task instructions by mocking a discussion where it acknowledges them. |
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| reit | Reinforce key elements in the instructions by repeating them. |
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| strict | Ask the model to answer by strictly following a given template. |
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| loose | Ask for just the final answer to be given following a given template. |
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| right | Asking the model to reach the right conclusion. |
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| info | Provide additional information to address common reasoning failures. |
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| name | Give the model a name by which we refer to it in conversation. |
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| pos | Provide the model with positive feedback before querying it. |
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### Performance Impact of All Prompt Modifications
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| | Precision | Recall | F1 | Template Stickiness |
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|----------------------------------------|---------------|---------------|---------------|------------------------|
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| _Baseline_ | _61.2_ | _70.6_ | _65.6_ | _79%_ |
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| _CoT_ | _72.6_ | _85.1_ | _78.4_ | _87%_ |
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| _Zero-CoT_ | _75.5_ | _88.3_ | _81.4_ | _65%_ |
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| _+rawinst_ | _80_ | _92.4_ | _85.8_ | _68%_ |
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| _+sysinst_ | _77.7_ | _90.9_ | _83.8_ | _69%_ |
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| _+bothinst_ | _81.9_ | _93.9_ | _87.5_ | _71%_ |
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| +bothinst+mock | 83.3 | 95.1 | 88.8 | 74% |
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| +bothinst+mock+reit | 83.8 | 95.5 | 89.3 | 75% |
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| _+bothinst+mock+reit+strict_ | _79.9_ | _93.7_ | _86.3_ | _**98%**_ |
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| _+bothinst+mock+reit+loose_ | _80.5_ | _94.8_ | _87.1_ | _95%_ |
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| +bothinst+mock+reit+right | 84 | 95.9 | 89.6 | 77% |
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| +bothinst+mock+reit+right+info | 84.9 | 96.5 | 90.3 | 77% |
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| +bothinst+mock+reit+right+info+name | 85.7 | 96.8 | 90.9 | 79% |
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| +bothinst+mock+reit+right+info+name+pos| **86.9** | **97** | **91.7** | 81% |
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Template stickiness refers to how frequently the model answers in the desired format.
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