Large Language Model Hacking: Quantifying the Hidden Risks of Using LLMs
  for Text Annotation
Authors: name: Joachim Baumann; name: Paul Röttger; name: Aleksandra Urman; name: Albert Wendsjö; name: Flor Miriam Plaza-del-Arco; name: Johannes B. Gruber; name: Dirk Hovy
Published: 2025-09-10 17:58:53

Abstract: Large language models (LLMs) are rapidly transforming social science research
by enabling the automation of labor-intensive tasks like data annotation and
text analysis. However, LLM outputs vary significantly depending on the
implementation choices made by researchers (e.g., model selection, prompting
strategy, or temperature settings). Such variation can introduce systematic
biases and random errors, which propagate to downstream analyses and cause Type
I, Type II, Type S, or Type M errors. We call this LLM hacking.
  We quantify the risk of LLM hacking by replicating 37 data annotation tasks
from 21 published social science research studies with 18 different models.
Analyzing 13 million LLM labels, we test 2,361 realistic hypotheses to measure
how plausible researcher choices affect statistical conclusions. We find
incorrect conclusions based on LLM-annotated data in approximately one in three
hypotheses for state-of-the-art models, and in half the hypotheses for small
language models. While our findings show that higher task performance and
better general model capabilities reduce LLM hacking risk, even highly accurate
models do not completely eliminate it. The risk of LLM hacking decreases as
effect sizes increase, indicating the need for more rigorous verification of
findings near significance thresholds. Our extensive analysis of LLM hacking
mitigation techniques emphasizes the importance of human annotations in
reducing false positive findings and improving model selection. Surprisingly,
common regression estimator correction techniques are largely ineffective in
reducing LLM hacking risk, as they heavily trade off Type I vs. Type II errors.
  Beyond accidental errors, we find that intentional LLM hacking is
unacceptably simple. With few LLMs and just a handful of prompt paraphrases,
anything can be presented as statistically significant.