A Bayesian Approach to Harnessing the Power of LLMs in Authorship Attribution
This addresses authorship attribution in forensic linguistics by improving accuracy with minimal data, though it is incremental as it applies existing LLMs to a new task.
The paper tackles authorship attribution by using pre-trained LLMs with Bayesian approaches to calculate probabilities that text entails previous writings of an author, achieving 85% accuracy in one-shot classification across ten authors on IMDb and blog datasets.
Authorship attribution aims to identify the origin or author of a document. Traditional approaches have heavily relied on manual features and fail to capture long-range correlations, limiting their effectiveness. Recent advancements leverage text embeddings from pre-trained language models, which require significant fine-tuning on labeled data, posing challenges in data dependency and limited interpretability. Large Language Models (LLMs), with their deep reasoning capabilities and ability to maintain long-range textual associations, offer a promising alternative. This study explores the potential of pre-trained LLMs in one-shot authorship attribution, specifically utilizing Bayesian approaches and probability outputs of LLMs. Our methodology calculates the probability that a text entails previous writings of an author, reflecting a more nuanced understanding of authorship. By utilizing only pre-trained models such as Llama-3-70B, our results on the IMDb and blog datasets show an impressive 85\% accuracy in one-shot authorship classification across ten authors. Our findings set new baselines for one-shot authorship analysis using LLMs and expand the application scope of these models in forensic linguistics. This work also includes extensive ablation studies to validate our approach.