# Scaling Down Semantic Leakage: Investigating Associative Bias in Smaller Language Models

arXiv: https://arxiv.org/abs/2501.06638 · HTML: https://mlsanity.com/paper/2501.06638/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 13 references that are in the map.
- First posted on arXiv 11 Jan 2025.

## Builds on (most cited first, 7 of 7)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers](https://mlsanity.com/paper/2210.17323/) (2022)
- [Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings](https://mlsanity.com/paper/1607.06520/) (2016)
- [Be Yourself: Bounded Attention for Multi-Subject Text-to-Image Generation](https://mlsanity.com/paper/2403.16990/) (2024)
- [DALLE-2 is Seeing Double: Flaws in Word-to-Concept Mapping in Text2Image Models](https://mlsanity.com/paper/2210.10606/) (2022)
- [Does Liking Yellow Imply Driving a School Bus? Semantic Leakage in Language Models](https://mlsanity.com/paper/2408.06518/) (2024)

## Built on by (most cited first, 2 of 2)

- [Am I Blue or Is My Hobby Counting Teardrops? Expression Leakage in Large Language Models as a Symptom of Irrelevancy Disruption](https://mlsanity.com/paper/2508.01708/) (2025)
- [Can You Keep a Secret? Involuntary Information Leakage in Language Model Writing](https://mlsanity.com/paper/2605.10794/) (2026)

## Abstract (arXiv)

Semantic leakage is a phenomenon recently introduced by Gonen et al. (2024). It refers to a situation in which associations learnt from the training data emerge in language model generations in an unexpected and sometimes undesired way. Prior work has focused on leakage in large language models (7B+ parameters). In this study, I use Qwen2.5 model family to explore whether smaller models, ranging from 500M to 7B parameters, demonstrate less semantic leakage due to their limited capacity for capturing complex associations. Building on the previous dataset from Gonen et al. (2024), I introduce a new dataset of color-focused prompts, categorized into specific types of semantic associations, to systematically evaluate the models' performance. Results indicate that smaller models exhibit less semantic leakage overall, although this trend is not strictly linear, with medium-sized models sometimes surpassing larger ones in leaking behavior. The dataset, the model generations, and the evaluation code are publicly available at https://github.com/smilni/semantic_leakage_project.