Papers by Adriano Koshiyama
From Text to Emoji: How PEFT-Driven Personality Manipulation Unleashes the Emoji Potential in LLMs (2025.findings-naacl)
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Navya Jain, Zekun Wu, Cristian Enrique Munoz Villalobos, Airlie Hilliard, Xin Guan, Adriano Koshiyama, Emre Kazim, Philip Colin Treleaven
| Challenge: | Methods like prompt-based In-Context Knowledge Editing and gradient-based Model Editor Networks (MEND) show irregularity and variability; IKE depends on the prompt, leading to variability and sensitivity; MEND yields inconsistent and gibberish outputs. |
| Approach: | They employ Opinion QA Based Parameter-Efficient Fine-Tuning (PEFT) to manipulate the Big Five personality traits: Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism. |
| Outcome: | The proposed methods show that they are more accurate than prompt-based IKE and gradient-based MEND outputs. |
HyPA-RAG: A Hybrid Parameter Adaptive Retrieval-Augmented Generation System for AI Legal and Policy Applications (2025.naacl-industry)
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Rishi Kalra, Zekun Wu, Ayesha Gulley, Airlie Hilliard, Xin Guan, Adriano Koshiyama, Philip Colin Treleaven
| Challenge: | Large Language Models (LLMs) face limitations due to outdated knowledge, hallucinations, and poor reasoning in complex contexts. |
| Approach: | They propose a Hybrid Parameter-Adaptive RAG system for the AI legal domain with NYC Local Law 144 as the test case. |
| Outcome: | The proposed system improves retrieval accuracy, response fidelity, and contextual precision on NYC Local Law 144 . Empirical evidence indicates that many AI tools overstate their ability to prevent hallucinations in legal and policy contexts. |
LibVulnWatch: A Deep Assessment Agent System and Leaderboard for Uncovering Hidden Vulnerabilities in Open-Source AI Libraries (2025.acl-srw)
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Zekun Wu, Seonglae Cho, Umar Mohammed, Cristian Enrique Munoz Villalobos, Kleyton Da Costa, Xin Guan, Theo King, Ze Wang, Emre Kazim, Adriano Koshiyama
| Challenge: | Open-source AI libraries present significant, underexamined risks spanning security, licensing, maintenance, supply chain integrity, and regulatory compliance. |
| Approach: | They propose a system that leverages large language models and agentic workflows to perform deep, evidence-based evaluations of open-source AI libraries. |
| Outcome: | The proposed system covers up to 88% of OpenSSF Scorecard checks and uncovers 19 additional risks per library. |
SAGED: A Holistic Bias-Benchmarking Pipeline for Language Models with Customisable Fairness Calibration (2025.coling-main)
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Xin Guan, Nate Demchak, Saloni Gupta, Ze Wang, Ediz Ertekin Jr., Adriano Koshiyama, Emre Kazim, Zekun Wu
| Challenge: | Existing benchmarks for large language models fail to detect bias due to limited scope, contamination, and lack of a fairness baseline. |
| Approach: | They propose a benchmarking pipeline to detect biases in large language models . they use metrics for max disparity, impact ratio, and bias concentration to analyze disparity . |
| Outcome: | SAGED(bias) is the first holistic benchmarking pipeline to address biases in large language models. |
JobFair: A Framework for Benchmarking Gender Hiring Bias in Large Language Models (2024.findings-emnlp)
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Ze Wang, Zekun Wu, Xin Guan, Michael Thaler, Adriano Koshiyama, Skylar Lu, Sachin Beepath, Ediz Ertekin, Maria Perez-Ortiz
| Challenge: | a framework for benchmarking hierarchical gender hiring bias in Large Language Models (LLMs) is developed to protect vulnerable demographic groups. |
| Approach: | They propose a framework for benchmarking hierarchical gender hiring bias in Large Language Models for resume scoring. |
| Outcome: | The proposed framework reveals significant issues of reverse gender hiring bias and overdebiasing in ten state-of-the-art LLMs. |