Yu Ying Chiu


2024

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Filtered Corpus Training (FiCT) Shows that Language Models Can Generalize from Indirect Evidence
Abhinav Patil | Jaap Jumelet | Yu Ying Chiu | Andy Lapastora | Peter Shen | Lexie Wang | Clevis Willrich | Shane Steinert-Threlkeld
Transactions of the Association for Computational Linguistics, Volume 12

This paper introduces Filtered Corpus Training, a method that trains language models (LMs) on corpora with certain linguistic constructions filtered out from the training data, and uses it to measure the ability of LMs to perform linguistic generalization on the basis of indirect evidence. We apply the method to both LSTM and Transformer LMs (of roughly comparable size), developing filtered corpora that target a wide range of linguistic phenomena. Our results show that while transformers are better qua LMs (as measured by perplexity), both models perform equally and surprisingly well on linguistic generalization measures, suggesting that they are capable of generalizing from indirect evidence.

2023

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Humanoid Agents: Platform for Simulating Human-like Generative Agents
Zhilin Wang | Yu Ying Chiu | Yu Cheung Chiu
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

Just as computational simulations of atoms, molecules and cells have shaped the way we study the sciences, true-to-life simulations of human-like agents can be valuable tools for studying human behavior. We propose Humanoid Agents, a system that guides Generative Agents to behave more like humans by introducing three elements of System 1 processing: Basic needs (e.g. hunger, health and energy), Emotion and Closeness in Relationships. Humanoid Agents are able to use these dynamic elements to adapt their daily activities and conversations with other agents, as supported with empirical experiments. Our system is designed to be extensible to various settings, three of which we demonstrate, as well as to other elements influencing human behavior (e.g. empathy, moral values and cultural background). Our platform also includes a Unity WebGL game interface for visualization and an interactive analytics dashboard to show agent statuses over time. Our platform is available on https://www.humanoidagents.com/ and code is on https://github.com/HumanoidAgents/HumanoidAgents