Affective connotations according to LLMs: implications for meaning measurement and cultural bias
Bibliographic record
Abstract
The affective connotations of words are central to meaning and important predictors of many social processes. As such, understanding the degree to which commercially-available generative language models (LLMs) replicate human judgements of affective connotations may help better understand human-model interactions. LLMs may also serve as useful tools for researchers seeking affective meaning estimates. We test the ability of three LLMs - GPT-4o, Mistral Large, and Llama 3.1 - to estimate human affective connotation ratings of words representing social identities, behaviours, modifiers, and settings in three language cultures: English (US), French (France), and German (Germany). We find that LLM ratings of terms correlate strongly with human ratings. However, their ratings tend to be overly extreme and patterns of correlations between meaning dimensions only loosely approximate those of human ratings. Consistent with previous findings of English-language and American biases in LLMs, we find that LLMs tend to perform better on English terms, though this pattern varies somewhat by meaning dimension and the type of term in question. We explore how LLMs might contribute to scholarship on affective connotations - by acting as tools for measurement - and how scholarship on affective connotations might contribute to generative language models - by guiding exploration of model biases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.044 | 0.270 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".