Scaling Down Semantic Leakage: Investigating Associative Bias in Smaller Language Models
- URL: http://arxiv.org/abs/2501.06638v1
- Date: Sat, 11 Jan 2025 21:03:22 GMT
- Title: Scaling Down Semantic Leakage: Investigating Associative Bias in Smaller Language Models
- Authors: Veronika Smilga,
- Abstract summary: I use Qwen2.5 model family to explore whether smaller models, ranging from 500M to 7B parameters, demonstrate less semantic leakage.
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.
- Score: 0.4944564023471818
- License:
- Abstract: 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.
Related papers
- Strong Model Collapse [16.071600606637908]
We consider a supervised regression setting and establish the existance of a strong form of the model collapse phenomenon.
Our results show that even the smallest fraction of synthetic data can lead to model collapse.
We investigate whether increasing model size, an approach aligned with current trends in training large language models, exacerbates or mitigates model collapse.
arXiv Detail & Related papers (2024-10-07T08:54:23Z) - Does Liking Yellow Imply Driving a School Bus? Semantic Leakage in Language Models [113.58052868898173]
We identify and characterize a phenomenon never discussed before, where models leak irrelevant information from the prompt into the generation in unexpected ways.
We propose an evaluation setting to detect semantic leakage both by humans and automatically, curate a diverse test suite for diagnosing this behavior, and measure significant semantic leakage in 13 flagship models.
arXiv Detail & Related papers (2024-08-12T22:30:55Z) - Stubborn Lexical Bias in Data and Models [50.79738900885665]
We use a new statistical method to examine whether spurious patterns in data appear in models trained on the data.
We apply an optimization approach to *reweight* the training data, reducing thousands of spurious correlations.
Surprisingly, though this method can successfully reduce lexical biases in the training data, we still find strong evidence of corresponding bias in the trained models.
arXiv Detail & Related papers (2023-06-03T20:12:27Z) - Compressing Sentence Representation with maximum Coding Rate Reduction [0.0]
In most natural language inference problems, sentence representation is needed for semantic retrieval tasks.
Due to space and time hardware limitations, there is a need to attain comparable results when using the smaller model.
We demonstrate that the new language model with reduced complexity and sentence embedding size can achieve comparable results on semantic retrieval benchmarks.
arXiv Detail & Related papers (2023-04-25T09:23:43Z) - Characterizing Attribution and Fluency Tradeoffs for Retrieval-Augmented
Large Language Models [6.425088990363101]
We examine the relationship between fluency and attribution in Large Language Models prompted with retrieved evidence.
We show that larger models tend to do much better in both fluency and attribution.
We propose a recipe that could allow smaller models to both close the gap with larger models and preserve the benefits of top-k retrieval.
arXiv Detail & Related papers (2023-02-11T02:43:34Z) - Dataless Knowledge Fusion by Merging Weights of Language Models [51.8162883997512]
Fine-tuning pre-trained language models has become the prevalent paradigm for building downstream NLP models.
This creates a barrier to fusing knowledge across individual models to yield a better single model.
We propose a dataless knowledge fusion method that merges models in their parameter space.
arXiv Detail & Related papers (2022-12-19T20:46:43Z) - Training Trajectories of Language Models Across Scales [99.38721327771208]
Scaling up language models has led to unprecedented performance gains.
How do language models of different sizes learn during pre-training?
Why do larger language models demonstrate more desirable behaviors?
arXiv Detail & Related papers (2022-12-19T19:16:29Z) - Learning to Model and Ignore Dataset Bias with Mixed Capacity Ensembles [66.15398165275926]
We propose a method that can automatically detect and ignore dataset-specific patterns, which we call dataset biases.
Our method trains a lower capacity model in an ensemble with a higher capacity model.
We show improvement in all settings, including a 10 point gain on the visual question answering dataset.
arXiv Detail & Related papers (2020-11-07T22:20:03Z) - Comparison of Interactive Knowledge Base Spelling Correction Models for
Low-Resource Languages [81.90356787324481]
Spelling normalization for low resource languages is a challenging task because the patterns are hard to predict.
This work shows a comparison of a neural model and character language models with varying amounts on target language data.
Our usage scenario is interactive correction with nearly zero amounts of training examples, improving models as more data is collected.
arXiv Detail & Related papers (2020-10-20T17:31:07Z) - not-MIWAE: Deep Generative Modelling with Missing not at Random Data [21.977065542645082]
We present an approach for building and fitting deep latent variable models (DLVMs) in cases where the missing process is dependent on the missing data.
Specifically, a deep neural network enables us to flexibly model the conditional distribution of the missingness pattern given the data.
We show on various kinds of data sets and missingness patterns that explicitly modelling the missing process can be invaluable.
arXiv Detail & Related papers (2020-06-23T10:06:21Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.