A Bayesian Approach to Harnessing the Power of LLMs in Authorship Attribution

• URL: http://arxiv.org/abs/2410.21716v1
• Date: Tue, 29 Oct 2024 04:14:23 GMT
• Title: A Bayesian Approach to Harnessing the Power of LLMs in Authorship Attribution
• Authors: Zhengmian Hu, Tong Zheng, Heng Huang,
• Abstract summary: Authorship attribution aims to identify the origin or author of a document. Large Language Models (LLMs) with their deep reasoning capabilities and ability to maintain long-range textual associations offer a promising alternative. Our results on the IMDb and blog datasets show an impressive 85% accuracy in one-shot authorship classification across ten authors.
• Score: 57.309390098903
• License:
• Abstract: 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.

Related papers

• Large Language Models Can Self-Improve in Long-context Reasoning [100.52886241070907]
  Large language models (LLMs) have achieved substantial progress in processing long contexts but still struggle with long-context reasoning. We propose ours, an approach specifically designed for this purpose. ours achieves superior performance compared to prior approaches that depend on data produced by human experts or advanced models.
  arXiv  Detail & Related papers  (2024-11-12T19:53:00Z)
• Context is Key: A Benchmark for Forecasting with Essential Textual Information [87.3175915185287]
  "Context is Key" (CiK) is a time series forecasting benchmark that pairs numerical data with diverse types of carefully crafted textual context. We evaluate a range of approaches, including statistical models, time series foundation models, and LLM-based forecasters. Our experiments highlight the importance of incorporating contextual information, demonstrate surprising performance when using LLM-based forecasting models, and also reveal some of their critical shortcomings.
  arXiv  Detail & Related papers  (2024-10-24T17:56:08Z)
• CopyLens: Dynamically Flagging Copyrighted Sub-Dataset Contributions to LLM Outputs [39.425944445393945]
  We introduce CopyLens, a framework to analyze how copyrighted datasets may influence Large Language Models responses. Experiments show that CopyLens improves efficiency and accuracy by 15.2% over our proposed baseline, 58.7% over prompt engineering methods, and 0.21 AUC over OOD detection baselines.
  arXiv  Detail & Related papers  (2024-10-06T11:41:39Z)
• ReMoDetect: Reward Models Recognize Aligned LLM's Generations [55.06804460642062]
  Large language models (LLMs) generate human-preferable texts. In this paper, we identify the common characteristics shared by these models. We propose two training schemes to further improve the detection ability of the reward model.
  arXiv  Detail & Related papers  (2024-05-27T17:38:33Z)
• Towards Next-Generation Steganalysis: LLMs Unleash the Power of Detecting Steganography [18.7168443402118]
  Linguistic steganography provides convenient implementation to hide messages, particularly with the emergence of AI generation technology. Existing methods are limited to finding distribution differences between steganographic texts and normal texts from the aspect of symbolic statistics. This paper propose to employ human-like text processing abilities of large language models (LLMs) to realize the difference from the aspect of human perception.
  arXiv  Detail & Related papers  (2024-05-15T04:52:09Z)
• Can Large Language Models Identify Authorship? [16.35265384114857]
  Large Language Models (LLMs) have demonstrated an exceptional capacity for reasoning and problem-solving. This work seeks to address three research questions: (1) Can LLMs perform zero-shot, end-to-end authorship verification effectively? (2) Are LLMs capable of accurately attributing authorship among multiple candidates authors (e.g., 10 and 20)?
  arXiv  Detail & Related papers  (2024-03-13T03:22:02Z)
• CoAnnotating: Uncertainty-Guided Work Allocation between Human and Large Language Models for Data Annotation [94.59630161324013]
  We propose CoAnnotating, a novel paradigm for Human-LLM co-annotation of unstructured texts at scale. Our empirical study shows CoAnnotating to be an effective means to allocate work from results on different datasets, with up to 21% performance improvement over random baseline.
  arXiv  Detail & Related papers  (2023-10-24T08:56:49Z)
• Neural Authorship Attribution: Stylometric Analysis on Large Language Models [16.63955074133222]
  Large language models (LLMs) such as GPT-4, PaLM, and Llama have significantly propelled the generation of AI-crafted text. With rising concerns about their potential misuse, there is a pressing need for AI-generated-text forensics.
  arXiv  Detail & Related papers  (2023-08-14T17:46:52Z)
• Evaluating and Explaining Large Language Models for Code Using Syntactic Structures [74.93762031957883]
  This paper introduces ASTxplainer, an explainability method specific to Large Language Models for code. At its core, ASTxplainer provides an automated method for aligning token predictions with AST nodes. We perform an empirical evaluation on 12 popular LLMs for code using a curated dataset of the most popular GitHub projects.
  arXiv  Detail & Related papers  (2023-08-07T18:50:57Z)
• Generation-driven Contrastive Self-training for Zero-shot Text Classification with Instruction-following LLM [31.25193238045053]
  We introduce a novel method, namely GenCo, which leverages the strong generative power of large language models to assist in training a smaller language model. In our method, an LLM plays an important role in the self-training loop of a smaller model in two important ways. It helps crafting additional high-quality training pairs, by rewriting input texts conditioned on predicted labels.
  arXiv  Detail & Related papers  (2023-04-24T07:35:38Z)

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.