6 citations · 13 across the 17 of their papers we have counts for
20 papers
Decomposing Irregular Time Series into Interpretable Temporal Patterns with Mixture-of-Experts
Jenna L. Ballard, Zijie Pan, Shivani Shekhar +6
Irregularly-spaced sequential data, such as transaction logs, encode multi-scale temporal structure that is critical for prediction but difficult to recover explicitly with standar…
TxnQA: Benchmarking Analytical Reasoning and Evidence Grounding over Transaction Event Sequences
Rabimba Karanjai, Yiming Bian, Yun-Shiuan Chuang +9
Analyzing merchant transaction histories requires dynamic temporal filtering, entity resolution, and aggregation over irregular event streams--operations poorly captured by static…
A Self-Improving Agent that Grows its Own DSL: Oracle-Free Operator Induction for Exact Question Answering
Yun-Shiuan Chuang, Yirou Ge, Yixi Li +6
A self-improving agent has to decide which new skills to keep, and in practice it keeps whatever earns a high reward, convinces a judge model, or compresses its library, even when…
State-Grounded Multi-Agent Synthetic Data Generation for Tool-Augmented LLMs
Rahul Khedar, Eshita, Sneha Teja Sree Reddy Thondapu +10
Training tool-augmented LLM agents requires large corpora of multi-turn, tool-grounded conversational data that is expensive to annotate, privacy-constrained in production settings…
Ada-RS: Adaptive Rejection Sampling for Selective Thinking
Yirou Ge, Yixi Li, Alec Chiu +8
Large language models (LLMs) are increasingly being deployed in cost and latency-sensitive settings. While chain-of-thought improves reasoning, it can waste tokens on simple reques…
Toward Scalable Verifiable Reward: Proxy State-Based Evaluation for Multi-turn Tool-Calling LLM Agents
Yun-Shiuan Chuang, Chaitanya Kulkarni, Alec Chiu +8
Interactive large language model (LLM) agents operating via multi-turn dialogue and multi-step tool calling are increasingly used in production. Benchmarks for these agents must bo…
DEBATE: A Large-Scale Benchmark for Evaluating Opinion Dynamics in Role-Playing LLM Agents
Yun-Shiuan Chuang, Ruixuan Tu, Chengtao Dai +9
Modeling opinion change through social interactions is important for understanding polarization, misinformation, and societal conflict. Recent work simulates opinion dynamics with…
Probing LLM World Models: Enhancing Guesstimation with Wisdom of Crowds Decoding
Yun-Shiuan Chuang, Sameer Narendran, Nikunj Harlalka +5
Guesstimation -- the task of making approximate quantitative estimates about objects or events -- is a common real-world skill, yet remains underexplored in large language model (L…
No Preference Left Behind: Group Distributional Preference Optimization
Binwei Yao, Zefan Cai, Yun-Shiuan Chuang +4
Preferences within a group of people are not uniform but follow a distribution. While existing alignment methods like Direct Preference Optimization (DPO) attempt to steer models t…
Optimizing Social Media Annotation of HPV Vaccine Skepticism and Misinformation Using Large Language Models: An Experimental Evaluation of In-Context Learning and Fine-Tuning Stance Detection Across Multiple Models
Luhang Sun, Varsha Pendyala, Yun-Shiuan Chuang +6
This paper leverages large-language models (LLMs) to experimentally determine optimal strategies for scaling up social media content annotation for stance detection on HPV vaccine-…
Beyond Demographics: Aligning Role-playing LLM-based Agents Using Human Belief Networks
Yun-Shiuan Chuang, Krirk Nirunwiroj, Zach Studdiford +6
Creating human-like large language model (LLM) agents is crucial for faithful social simulation. Having LLMs role-play based on demographic information sometimes improves human lik…
The Delusional Hedge Algorithm as a Model of Human Learning from Diverse Opinions
Yun-Shiuan Chuang, Jerry Zhu, Timothy T. Rogers
Whereas cognitive models of learning often assume direct experience with both the features of an event and with a true label or outcome, much of everyday learning arises from heari…
Learning interactions to boost human creativity with bandits and GPT-4
Ara Vartanian, Xiaoxi Sun, Yun-Shiuan Chuang +3
This paper considers how interactions with AI algorithms can boost human creative thought. We employ a psychological task that demonstrates limits on human creativity, namely seman…
Evolving Domain Adaptation of Pretrained Language Models for Text Classification
Yun-Shiuan Chuang, Yi Wu, Dhruv Gupta +7
Adapting pre-trained language models (PLMs) for time-series text classification amidst evolving domain shifts (EDS) is critical for maintaining accuracy in applications like stance…
The Wisdom of Partisan Crowds: Comparing Collective Intelligence in Humans and LLM-based Agents
Yun-Shiuan Chuang, Siddharth Suresh, Nikunj Harlalka +6
Human groups are able to converge on more accurate beliefs through deliberation, even in the presence of polarization and partisan bias -- a phenomenon known as the "wisdom of part…
Simulating Opinion Dynamics with Networks of LLM-based Agents
Yun-Shiuan Chuang, Agam Goyal, Nikunj Harlalka +6
Accurately simulating human opinion dynamics is crucial for understanding a variety of societal phenomena, including polarization and the spread of misinformation. However, the age…
Tutorials on Stance Detection using Pre-trained Language Models: Fine-tuning BERT and Prompting Large Language Models
Yun-Shiuan Chuang
This paper presents two self-contained tutorials on stance detection in Twitter data using BERT fine-tuning and prompting large language models (LLMs). The first tutorial explains…
Computational Agent-based Models in Opinion Dynamics: A Survey on Social Simulations and Empirical Studies
Yun-Shiuan Chuang, Timothy T. Rogers
Understanding how an individual changes its attitude, belief, and opinion due to other people's social influences is vital because of its wide implications. A core methodology that…
Adoption and implication of the Biased-Annotator Competence Estimation (BACE) model into COVID-19 vaccine Twitter data: Human annotation for latent message features
Luhang Sun, Yun-Shiuan Chuang, Yibing Sun +1
Traditional quantitative content analysis approach (human coding method) has weaknesses, such as assuming all human coders are equally accurate once the intercoder reliability for…
Using Machine Teaching to Investigate Human Assumptions when Teaching Reinforcement Learners
Yun-Shiuan Chuang, Xuezhou Zhang, Yuzhe Ma +3
Successful teaching requires an assumption of how the learner learns - how the learner uses experiences from the world to update their internal states. We investigate what expectat…