Papers by Saranya Venkatraman
Beyond Checkmate: Exploring the Creative Choke Points for AI Generated Texts (2025.emnlp-main)
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| Challenge: | Recent work on detecting LLM-generated text (AI text) has raised concerns about potential misuse . a new study examines the nuanced distinctions between human and AI texts . |
| Approach: | They analyze human-AI text differences across body, intro, conclusion segments . human texts exhibit greater stylistic variation across segments, they show . |
| Outcome: | The findings will inform their viability and boundaries as effective creative assistants to humans. |
GPT-who: An Information Density-based Machine-Generated Text Detector (2024.findings-naacl)
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| Challenge: | Large Language Models (LLMs) generate misinformation, memorized content, plagiarized content, toxic speech, and hallucinated content. |
| Approach: | They propose a statistical detector that uses UID to model the unique statistical signature of each LLM and human author for accurate detection. |
| Outcome: | The proposed method outperforms state-of-the-art detectors by over 20% across domains. |
How do decoding algorithms distribute information in dialogue responses? (2023.findings-eacl)
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| Challenge: | Using different decoding algorithms, we find that human dialogue generation is beneficial for adherence to the Uniform Information Density principle. |
| Approach: | They investigate whether decoding algorithms implicitly follow the Uniform Information Density principle by distributing information evenly in utterances. |
| Outcome: | The proposed method encourages non-uniform responses, but under low/high surprisal conditions, resulting in poor quality responses. |
CollabStory: Multi-LLM Collaborative Story Generation and Authorship Analysis (2025.findings-naacl)
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| Challenge: | Existing studies on LLM-LLM collaboration for open-ended tasks have focused on human-LLm interaction. |
| Approach: | They propose to generate a dataset exclusively for LLMs to explore multi-LLM collaboration scenarios . they extend their authorship-related tasks for multi-llm settings and extend their baselines . |
| Outcome: | The authors extend authorship-related tasks for multi-LLM settings and present baselines for LLM-LLMS collaboration. |
A Ship of Theseus: Curious Cases of Paraphrasing in LLM-Generated Texts (2024.acl-long)
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Nafis Irtiza Tripto, Saranya Venkatraman, Dominik Macko, Robert Moro, Ivan Srba, Adaku Uchendu, Thai Le, Dongwon Lee
| Challenge: | Using a computational approach, we discover that diminishing performance in text classification models is closely associated with the extent of deviation from the original author’s style. |
| Approach: | They propose to use large language models to determine whether a text retains original authorship when it undergoes numerous paraphrasing iterations. |
| Outcome: | The results suggest that authorship should be task-dependent . |
Beemo: Benchmark of Expert-edited Machine-generated Outputs (2025.naacl-long)
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Ekaterina Artemova, Jason S Lucas, Saranya Venkatraman, Jooyoung Lee, Sergei Tilga, Adaku Uchendu, Vladislav Mikhailov
| Challenge: | Existing benchmarks for machine-generated texts (MGTs) include single-author texts (human-written and machine-generated). |
| Approach: | They propose to benchmark machine-generated outputs (Beemo) which includes 6.5k texts written by humans, generated by ten instruction-finetuned LLMs, and edited by experts for various use cases. |
| Outcome: | The proposed benchmark includes 6.5k texts written by humans, generated by ten instruction-finetuned LLMs, and edited by experts for various use cases, ranging from creative writing to summarization. |
Catch Me If You GPT: Tutorial on Deepfake Texts (2024.naacl-tutorials)
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| Challenge: | In recent years, natural language generation (NLG) techniques have advanced, but pose new security risks . this tutorial will be 3 hours long with a mix of lecture and hands-on examples for interactive audience participation. |
| Approach: | They present a tutorial on the security of natural language generation (NLG) they review the latest literature on the detection and obfuscation of deepfake text authorships . |
| Outcome: | This tutorial reviews the latest literature on the detection and obfuscation of deepfake text authorships. |
The Sentiment Problem: A Critical Survey towards Deconstructing Sentiment Analysis (2023.emnlp-main)
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Pranav Venkit, Mukund Srinath, Sanjana Gautam, Saranya Venkatraman, Vipul Gupta, Rebecca Passonneau, Shomir Wilson
| Challenge: | Existing research reveals a notable absence of interdisciplinary endeavors to comprehend the social dimensions of sentiment analysis, encompassing aspects like emotion and fairness. |
| Approach: | They propose an ethics sheet encompassing critical inquiries to guide practitioners in ensuring equitable utilization of SA. |
| Outcome: | The proposed ethics sheet outlines the importance of adopting an interdisciplinary approach to defining sentiment in SA and offers a pragmatic solution for its implementation. |