Summary of From Text to Insight: Leveraging Large Language Models For Performance Evaluation in Management, by Ning Li et al.
From Text to Insight: Leveraging Large Language Models for Performance Evaluation in Management
by Ning Li, Huaikang Zhou, Mingze Xu
First submitted to arxiv on: 9 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET); Human-Computer Interaction (cs.HC); General Economics (econ.GN)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores the use of Large Language Models (LLMs), specifically GPT-4, to enhance objectivity in organizational task performance evaluations. Comparative analyses across two studies demonstrate that LLMs can be a reliable alternative to human raters for evaluating knowledge-based performance outputs, which are crucial contributions of knowledge workers. The results show that GPT ratings are comparable to human ratings but exhibit higher consistency and reliability. Furthermore, combined multiple GPT ratings on the same performance output correlate strongly with aggregated human performance ratings, mirroring the consensus principle in performance evaluation literature. However, LLMs are prone to contextual biases, such as the halo effect, similar to human evaluative biases. The study highlights both the potential and limitations of LLMs, contributing to the discourse on AI’s role in management studies and setting a foundation for future research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how Large Language Models (LLMs) can help make organizational task performance evaluations fairer. It compares two studies that use GPT-4 to see if it can do just as well as human raters. The results show that LLMs are consistent and reliable, and can even agree with humans on what’s good or bad. However, they also have some biases, like humans do. This study shows that while LLMs can be useful for certain types of evaluations, we need to understand their limits too. |
Keywords
» Artificial intelligence » Discourse » Gpt
