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Prompt-Engineering-Guide
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Adding grad job classification case study paper and summary

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{ {
"pal": "Program-Aided Language Models", "pal": "Program-Aided Language Models",
"generating": "Generating Data" "generating": "Generating Data",
"workplace_casestudy": "Graduate Job Classifcation Case Study"
} }

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# LLMs as Graduate Job Classifiers Case-Study
[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`).
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
| Short name | Description |
|------------|----------------------------------------------------------------------------|
| 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
| | Precision | Recall | F1 | Template Stickiness |
|----------------------------------------|---------------|---------------|---------------|------------------------|
| _Baseline_ | _61.2_ | _70.6_ | _65.6_ | _79%_ |
| _CoT_ | _72.6_ | _85.1_ | _78.4_ | _87%_ |
| _Zero-CoT_ | _75.5_ | _88.3_ | _81.4_ | _65%_ |
| _+rawinst_ | _80_ | _92.4_ | _85.8_ | _68%_ |
| _+sysinst_ | _77.7_ | _90.9_ | _83.8_ | _69%_ |
| _+bothinst_ | _81.9_ | _93.9_ | _87.5_ | _71%_ |
| +bothinst+mock | 83.3 | 95.1 | 88.8 | 74% |
| +bothinst+mock+reit | 83.8 | 95.5 | 89.3 | 75% |
| _+bothinst+mock+reit+strict_ | _79.9_ | _93.7_ | _86.3_ | _**98%**_ |
| _+bothinst+mock+reit+loose_ | _80.5_ | _94.8_ | _87.1_ | _95%_ |
| +bothinst+mock+reit+right | 84 | 95.9 | 89.6 | 77% |
| +bothinst+mock+reit+right+info | 84.9 | 96.5 | 90.3 | 77% |
| +bothinst+mock+reit+right+info+name | 85.7 | 96.8 | 90.9 | 79% |
| +bothinst+mock+reit+right+info+name+pos| **86.9** | **97** | **91.7** | 81% |
**Impact of the various prompt modifications.**

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@ -133,6 +133,7 @@ The following are the latest papers (sorted by release date) on prompt engineeri
- [Large Language Models and Simple, Stupid Bugs](https://arxiv.org/abs/2303.11455) (March 2023) - [Large Language Models and Simple, Stupid Bugs](https://arxiv.org/abs/2303.11455) (March 2023)
- [Can Generative Pre-trained Transformers (GPT) Pass Assessments in Higher Education Programming Courses?](https://arxiv.org/abs/2303.09325) (Mar 2023) - [Can Generative Pre-trained Transformers (GPT) Pass Assessments in Higher Education Programming Courses?](https://arxiv.org/abs/2303.09325) (Mar 2023)
- [SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models](https://arxiv.org/abs/2303.08896) (Mar 2023) - [SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models](https://arxiv.org/abs/2303.08896) (Mar 2023)
- [Large Language Models in the Workplace: A Case Study on Prompt Engineering for Job Type Classification](https://arxiv.org/abs/2303.07142) (March 2023)
- [ICL-D3IE: In-Context Learning with Diverse Demonstrations Updating for Document Information Extraction](https://arxiv.org/abs/2303.05063) (March 2023) - [ICL-D3IE: In-Context Learning with Diverse Demonstrations Updating for Document Information Extraction](https://arxiv.org/abs/2303.05063) (March 2023)
- [MathPrompter: Mathematical Reasoning using Large Language Models](https://arxiv.org/abs/2303.05398) (March 2023) - [MathPrompter: Mathematical Reasoning using Large Language Models](https://arxiv.org/abs/2303.05398) (March 2023)
- [Prompt-Based Learning for Thread Structure Prediction in Cybersecurity Forums](https://arxiv.org/abs/2303.05400) (March 2023) - [Prompt-Based Learning for Thread Structure Prediction in Cybersecurity Forums](https://arxiv.org/abs/2303.05400) (March 2023)