Papers by Ajay Patel
DataDreamer: A Tool for Synthetic Data Generation and Reproducible LLM Workflows (2024.acl-long)
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| Challenge: | Large language models (LLMs) have become a dominant tool for NLP researchers in a wide range of tasks. |
| Approach: | They propose an open source Python library that allows researchers to write simple code to implement powerful LLM workflows. |
| Outcome: | The proposed library is open source and can be used to implement powerful LLM workflows. |
Quantifying Misattribution Unfairness in Authorship Attribution (2025.acl-short)
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| Challenge: | Authorship misattribution can have profound consequences in real life . authors are considered as potential authors in forensic settings . |
| Approach: | They propose a measure to quantify the unfairness of authorship attribution systems . authors find that authors are more likely to be misattributed than others . |
| Outcome: | The proposed model shows that some authors are more likely to be misattributed than others. |
Latent Space Interpretation for Stylistic Analysis and Explainable Authorship Attribution (2025.coling-main)
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| Challenge: | Recent authorship attribution methods learn authorship representations of text in a latent, uninterpretable space, which hinders their usability in real-world applications. |
| Approach: | They propose a method for interpreting latent authorship representations by identifying representative points in the latent space and leveraging large language models to generate informative natural language descriptions of the writing style associated with each point. |
| Outcome: | The proposed method outperforms baseline methods on the authorship attribution task by +20% on average when aided with explanations from the method. |
Learning Interpretable Style Embeddings via Prompting LLMs (2023.findings-emnlp)
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| Challenge: | Prior work has treated the style of a text as separable from the content. |
| Approach: | They use prompting to perform stylometry on a large number of texts to generate a synthetic stylometric dataset. |
| Outcome: | The proposed model trains human-interpretable representations on a large stylometric dataset and a linguistic model for style representation learning. |
Magnitude: A Fast, Efficient Universal Vector Embedding Utility Package (D18-2)
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| Challenge: | Magnitude is an open source Python package that performs common operations up to 6,000 times faster than Gensim. |
| Approach: | They present a Python tool for utilizing vector embeddings that performs common operations up to 6,000 times faster than Gensim. |
| Outcome: | The Magnitude package performs common operations up to 6,000 times faster than Gensim and introduces several novel features for improved robustness like out-of-vocabulary lookups. |
StyleDistance: Stronger Content-Independent Style Embeddings with Synthetic Parallel Examples (2025.naacl-long)
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Ajay Patel, Jiacheng Zhu, Justin Qiu, Zachary Horvitz, Marianna Apidianaki, Kathleen McKeown, Chris Callison-Burch
| Challenge: | Existing methods for embedding text are limited by the imperfect nature of data acquired under such assumptions. |
| Approach: | They propose a new approach to training stronger content-independent style embeddings using a synthetic dataset of near-exact paraphrases with controlled style variations. |
| Outcome: | The proposed model outperforms existing methods in real-world benchmarks and outperformed leading style representations in downstream applications. |
mStyleDistance: Multilingual Style Embeddings and their Evaluation (2025.findings-acl)
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| Challenge: | Multilingual StyleDistance embeddings are useful for stylistic analysis and style transfer, but they only exist for English. |
| Approach: | They propose a method that can generate style embeddings in new languages using synthetic data and a contrastive loss. |
| Outcome: | The proposed method outperforms existing style embeddings on these benchmarks and generalizes well to unseen features and languages. |
Scaling Text-Rich Image Understanding via Code-Guided Synthetic Multimodal Data Generation (2025.acl-long)
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Yue Yang, Ajay Patel, Matt Deitke, Tanmay Gupta, Luca Weihs, Andrew Head, Mark Yatskar, Chris Callison-Burch, Ranjay Krishna, Aniruddha Kembhavi, Christopher Clark
| Challenge: | Vision-language models struggle to understand text-rich images due to the scarcity of diverse text-only large language data. |
| Approach: | They propose a framework that leverages the coding capabilities of text-only large language models to create synthetic text-rich multimodal data. |
| Outcome: | The proposed framework can generate high-quality instruction-tuning data using Python, HTML, LaTeX and other languages. |
TinyStyler: Efficient Few-Shot Text Style Transfer with Authorship Embeddings (2024.findings-emnlp)
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| Challenge: | Existing methods for text style transfer rely on few-shot capabilities of large language models or complex controllable text generation approaches that are inefficient and underperform on fluency metrics. |
| Approach: | They propose a lightweight but effective approach which leverages a small language model and pre-trained authorship embeddings to perform efficient, few-shot text style transfer. |
| Outcome: | The proposed method outperforms strong approaches such as GPT-4 and performs form attribute style transfer with automatic and human evaluations. |