Papers by Philip Colin Treleaven
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| Challenge: | Large Language Models (LLMs) face limitations due to outdated knowledge, hallucinations, and poor reasoning in complex contexts.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. |