Leveraging large language models for automated performance appraisals: Opportunities and challenges
Fuente:
Zenodo
Salvato in:
| Autore principale: | |
|---|---|
| Natura: | Recurso digital |
| Lingua: | inglese |
| Pubblicazione: |
Zenodo
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866901391016984576 |
|---|---|
| author | Kuchibhotla, Sri |
| author_facet | Kuchibhotla, Sri |
| contents | <p>One major issue with traditional performance appraisals is inefficiency, bias and subjectivity. Oftentimes large language models (LLMs) like GPT-4 offer a promising approach to standardize performance evaluations which leverage structured and unstructured feedback for data-driven assessments. In this study, a data set with structured and unstructured data is taken and fed into GPT-4 to analyze self-evaluations and mid-year performance reviews to automate the appraisal process and compare it to human evaluations. Although GPT-4 is generally accurate and is similar to human assessment, the main challenge lies in the non-quantifiable factors such as workplace dynamics and lack of emotional intelligence. Although AI models have a much more accurate prediction rate than manual performance appraisals, there is always a need for a human-in-the-loop (HITL) approach to help AI perform better. This study focuses on how human-in-the-loop (HITL) can help AI-based performance appraisals by bringing in non-quantifiable factors such as workplace dynamics and conflict resolution within the employee data.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17051412 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Leveraging large language models for automated performance appraisals: Opportunities and challenges Kuchibhotla, Sri Artificial Intelligence Large Language Models in HR Performance Appraisals Human Resources AI Bias Human-in-the-Loop <p>One major issue with traditional performance appraisals is inefficiency, bias and subjectivity. Oftentimes large language models (LLMs) like GPT-4 offer a promising approach to standardize performance evaluations which leverage structured and unstructured feedback for data-driven assessments. In this study, a data set with structured and unstructured data is taken and fed into GPT-4 to analyze self-evaluations and mid-year performance reviews to automate the appraisal process and compare it to human evaluations. Although GPT-4 is generally accurate and is similar to human assessment, the main challenge lies in the non-quantifiable factors such as workplace dynamics and lack of emotional intelligence. Although AI models have a much more accurate prediction rate than manual performance appraisals, there is always a need for a human-in-the-loop (HITL) approach to help AI perform better. This study focuses on how human-in-the-loop (HITL) can help AI-based performance appraisals by bringing in non-quantifiable factors such as workplace dynamics and conflict resolution within the employee data.</p> |
| title | Leveraging large language models for automated performance appraisals: Opportunities and challenges |
| topic | Artificial Intelligence Large Language Models in HR Performance Appraisals Human Resources AI Bias Human-in-the-Loop |
| url | https://doi.org/10.5281/zenodo.17051412 |