Large Language Model Hacking: Quantifying the Hidden Risks of Using LLMs for Text Annotation

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Conflicts of Interest

Identified Weaknesses

Rating Explanation

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Paper Summary

Paperzilla title

LLM Annotations Can Lead to Wrong Conclusions: One in Three Studies Affected by 'LLM Hacking'

This study finds a substantial risk of drawing incorrect conclusions in social science research when using Large Language Models (LLMs) for text annotation, with an average of one in three hypotheses leading to false conclusions due to variations in LLM configuration ('LLM hacking'). Even highly accurate LLMs are susceptible, and intentional manipulation to achieve desired outcomes is alarmingly easy.

Possible Conflicts of Interest

None identified

Identified Weaknesses

Assumption of noise-free ground truth

The study assumes human annotations are perfect, which is unlikely in reality. This might overestimate the error rate attributed solely to the LLMs.

Limited configuration space

The selection of models, prompts, and other settings explored might not represent the full spectrum used by researchers, potentially underestimating the true extent of LLM hacking risk.

Focus on p<0.05

Relying on a strict p-value threshold can be problematic, especially given the demonstrated instability of LLM results near significance boundaries.

Rating Explanation

This paper reveals a critical, previously overlooked issue in computational social science and quantifies the risks associated with using LLMs for data annotation. The methodology is rigorous, involving a large-scale replication study across diverse tasks and models. While there are limitations regarding the ground truth assumption and the explored configuration space, the findings are substantial and have significant implications for research practice. The paper also offers practical recommendations to mitigate the identified risks, which enhances its value to the scientific community.

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