Using Semantically Unrelated and Opposite Terms for In-Context Learning: A Case Study in Identifying Political Aversion in Tweets
Bibliographic record
Abstract
We investigate how semantic priors embedded in generative large language models (LLMs) interact with concept definitions in prompts, using political aversion detection as a case study.Through systematic variations in the wording of the definition of political aversion-replacing key words with opposite terms, semantically unrelated terms, or deliberately nonsensical strings-we examine how models process and apply these manipulated definitions in classification tasks.Our results show that certain LLMs maintain consistent performance across different prompt configurations, regardless of which terms are used or whether examples are included.Strong classification performances even with nonsensical definitions suggest these models may sometimes rely more on patterns in target content than definitions given in prompts.These findings challenge conventional assumptions about prompt engineering and raise important questions about how LLMs utilize information in prompts for classification decisions, while underscoring the need for careful validation when applying these methods to social and political science measurement tasks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".