Introducing InstructAV: Advancements in Authorship Verification

Authorship Verification (AV) is the task of determining if two texts were written by the same person. It is important in areas like forensics, literature, and online security. Traditional methods have focused on analyzing writing styles, using characteristics like word length and frequency of certain words. However, machine learning techniques, especially deep learning Models like BERT and RoBERTa, have shown to be more effective. They can detect complex patterns in text that help distinguish between different authors.

Despite advancements, many existing AV models mainly focus on whether texts match or not, without giving clear reasons for their decisions. This is a problem because understanding why a model makes a certain decision is important for trust in its outputs. It is also crucial for identifying and correcting any biases in these models. Therefore, AI models should aim for both accurate predictions and clear Explanations.

#The InstructAV Framework

This paper introduces InstructAV, a new approach for AV tasks. InstructAV not only aims to accurately determine if two texts are by the same author but also provides clear explanations for its decisions. A key feature of InstructAV is its ability to combine accurate classification with understandable explanations. This allows users to see why specific conclusions are drawn.

The framework has been tested using several Datasets, showing that it performs well on AV tasks. InstructAV delivers reliable and accurate predictions while also offering detailed reasons for its decisions.

#Key Contributions

The main contributions of this paper include:

1. Introducing the InstructAV framework, which accurately determines if two texts share an author and provides trustworthy explanations.
2. Creating three datasets designed for instruction-tuning, filled with reliable linguistic explanations suitable for AV tasks.
3. Demonstrating through evaluations that InstructAV effectively predicts authorship and offers meaningful explanations.

#Related Work

Over the last two decades, AV has seen major changes, moving from traditional methods based on writing style to using machine learning. Traditional machine learning approaches, like support vector machines, have not been very effective. Recent advancements involve language models that use context, such as BERT and T5. These newer methods have proven to be more successful than older techniques.

Past methods, including BERT, have been crucial for AV tasks but often fail to explain their decisions. The need for explainable AI has led to techniques like PromptAV, which uses LLMs to provide more understandable analyses. PromptAV has shown improvements over older methods, especially in providing reasoning behind decisions.

Despite these advancements, existing models still face challenges. They often depend on limited demonstrations, which affects the quality and relevance of their explanations. This highlights the need for improved techniques that can deliver both accurate Classifications and useful explanations across various contexts.

To address these challenges, InstructAV adopts a fine-tuning approach that enhances both classification accuracy and explanation quality for AV tasks.

#Parameter-Efficient Fine-Tuning

Large language models (LLMs) like GPT-3 bring significant improvements to AI but are often hard to deploy due to their high resource requirements. Parameter-efficient fine-tuning (PEFT) provides a solution by adjusting only a small number of model parameters for specific tasks, saving resources.

One method within PEFT is the use of adapters, which are small modules added to existing models. Adapters allow for efficient customization without needing to retrain the entire model. A popular adapter method, Low-Rank Adaptation (LoRA), fine-tunes model parameters efficiently while preserving the core capabilities of the LLMs.

In our research, we utilize LoRA to enhance the performance of InstructAV on AV tasks.

#Explanation Data Collection

To create quality explanations, we collected data from three common AV datasets: IMDB, Twitter, and Yelp Reviews. This varied selection allows for a comprehensive examination of different writing styles.

For explanation generation, we used ChatGPT to analyze writing samples and produce explanations based on defined linguistic features. By combining these explanations with classification labels, we ensure relevant data that improves the explanatory function of InstructAV.

#Consistency Verification

It's crucial for the explanations provided by the model to align with its classification decisions. We developed a verification process to check the consistency between the model’s explanations and its predictions. This step enhances trust in the model’s outputs by ensuring that explanations make sense in relation to the classifications.

#Fine-Tuning with LoRA

Adapting LLMs to AV tasks can be resource-intensive. To minimize this, we implement LoRA to fine-tune the models effectively. LoRA updates only specific weight parameters, reducing the need for extensive resources while retaining the model's general strengths.

#Experiment Settings

We evaluated InstructAV by using three datasets: IMDB62, Twitter, and Yelp Reviews. Each dataset was chosen for its diversity, allowing for a better understanding of the model’s capabilities.

We created two types of dataset settings:

1. Classification: This setup includes a question and two texts. The model focuses on determining if they are from the same author.
2. Classification and Explanation: This setting adds linguistic analysis to the classification, enabling the model to produce explanations alongside predictions.

These settings assist in evaluating how well the model performs in AV tasks.

#Baselines

For our classification task, we compared InstructAV against established models like BERT and its variations. These models are commonly used for AV classification tasks.

For the explanation tasks, we utilized autoregressive models like GPT, specifically using PromptAV techniques to assess their performance against InstructAV.

#Evaluation Metrics

We used accuracy as the main metric for measuring how well InstructAV can determine authorship. For explaining decisions, we evaluated the quality of the linguistic analysis produced by the models. Since explanation quality is subjective, we employed both automated metrics and human evaluations.

#Automatic Evaluation for Explanations

Through automatic evaluation, we measured how similar the generated explanations were to established standards. We utilized various metrics to gauge content coverage, structural fluency, and semantic quality.

#Human Evaluation for Explanations

To complement the automated approach, we conducted human evaluations. Evaluators assessed the explanations based on criteria such as coverage, relevance, reasonableness, and persuasiveness.

#Experiment Results

#Classification Results

InstructAV has been evaluated on its ability to perform AV tasks. The results indicated that InstructAV outperformed baseline models across different datasets, achieving notable improvements in classification accuracy.

#Automatic Evaluation Results on Explanations

The evaluations revealed that InstructAV consistently surpassed the other models in explanation quality, demonstrating that it can achieve better content overlap and maintain logical coherence.

#Human Evaluation Results on Explanations

The human evaluation showed that InstructAV provided the highest scores compared to the baseline models, confirming its ability to produce accurate and relevant explanations.

#Correlation between Explanation and Classification

We analyzed the relationship between explanation quality and classification accuracy, finding that higher-quality explanations correlated with better classification results. This suggests that the training of InstructAV enhances both functions simultaneously.

#Ablation Study

Our research shows that InstructAV represents a substantial advancement in the AV field. It not only improves classification accuracy but also generates clear explanations for its decisions. The work presented in this paper marks a significant step forward, highlighting the importance of dual emphasis on accuracy and explanation quality in the AV domain.

#Future Work

While InstructAV achieves impressive results, it faces limitations in terms of generating long explanations efficiently. Future research will aim to improve the speed and efficiency of the explanation generation process.

#Case Study for InstructAV

Selected examples illustrate how InstructAV provides both classification predictions and detailed linguistic feature explanations. These cases demonstrate the model’s ability to deliver accurate classifications alongside clear reasons, enhancing user trust in its outputs.

#Conclusion

InstructAV stands out as a state-of-the-art solution for authorship verification. With its strong focus on both classification performance and the quality of explanations, it sets a new benchmark in the field. The contributions of InstructAV, including the creation of new datasets and the effectiveness in generating coherent explanations, signal exciting advancements in authorship verification research.