131 citations · 144 across the 24 of their papers we have counts for
24 papers
Agentic Chain-of-Thought Steering for Efficient and Controllable LLM Reasoning
Yu Xia, Zhouhang Xie, Xin Xu +4
Large language models improve final-answer accuracy through extended chain-of-thought reasoning, but often spend tokens inefficiently and offer little inference-time control. Exist…
PACEvolve++: Improving Test-time Learning for Evolutionary Search Agents
Minghao Yan, Bo Peng, Benjamin Coleman +11
Large language models have become drivers of evolutionary search, but most systems rely on a fixed, prompt-elicited policy to sample next candidates. This limits adaptation in prac…
AgenticTagger: Structured Item Representation for Recommendation with LLM Agents
Zhouhang Xie, Bo Peng, Zhankui He +11
High-quality representations are a core requirement for effective recommendation. In this work, we study the problem of LLM-based descriptor generation, i.e., keyphrase-like natura…
Evaluation on Entity Matching in Recommender Systems
Zihan Huang, Rohan Surana, Zhouhang Xie +3
Entity matching is a crucial component in various recommender systems, including conversational recommender systems (CRS) and knowledge-based recommender systems. However, the lack…
PACEvolve: Enabling Progress-Aware Consistent Evolution
Minghao Yan, Bo Peng, Benjamin Coleman +13
Self-evolving agents powered by Large Language Models (LLMs) have emerged as a promising direction across diverse domains, including code optimization and scientific discovery, yet…
Pluralistic Off-policy Evaluation and Alignment
Chengkai Huang, Junda Wu, Zhouhang Xie +6
Personalized preference alignment for LLMs with diverse human preferences requires evaluation and alignment methods that capture pluralism. Most existing preference alignment datas…
World Food Atlas Project
Ali Rostami, Z Xie, A Ishino +3
A coronavirus pandemic is forcing people to be "at home" all over the world. In a life of hardly ever going out, we would have realized how the food we eat affects our bodies. What…
Improving In-Context Learning with Reasoning Distillation
Nafis Sadeq, Xin Xu, Zhouhang Xie +4
Language models rely on semantic priors to perform in-context learning, which leads to poor performance on tasks involving inductive reasoning. Instruction-tuning methods based on…
A Survey on Personalized and Pluralistic Preference Alignment in Large Language Models
Zhouhang Xie, Junda Wu, Yiran Shen +9
Personalized preference alignment for large language models (LLMs), the process of tailoring LLMs to individual users' preferences, is an emerging research direction spanning the a…
In-context Ranking Preference Optimization
Junda Wu, Rohan Surana, Zhouhang Xie +6
Recent developments in Direct Preference Optimization (DPO) allow large language models (LLMs) to function as implicit ranking models by maximizing the margin between preferred and…
Interacting Chern insulator transition on the sphere: revealing the Gross-Neveu-Yukawa criticality
Zhi-Qiang Gao, Taige Wang, Dung-Hai Lee
In two spatial dimensions, the transition between topological and trivial Chern insulators exemplifies a class of beyond-Landau critical phenomena. We show that the interaction-dri…
Latent Factor Models Meets Instructions: Goal-conditioned Latent Factor Discovery without Task Supervision
Zhouhang Xie, Tushar Khot, Bhavana Dalvi Mishra +4
Instruction-following LLMs have recently allowed systems to discover hidden concepts from a collection of unstructured documents based on a natural language description of the purp…
From Selection to Generation: A Survey of LLM-based Active Learning
Yu Xia, Subhojyoti Mukherjee, Zhouhang Xie +31
Active Learning (AL) has been a powerful paradigm for improving model efficiency and performance by selecting the most informative data points for labeling and training. In recent…
GUI Agents: A Survey
Dang Nguyen, Jian Chen, Yu Wang +27
Graphical User Interface (GUI) agents, powered by Large Foundation Models, have emerged as a transformative approach to automating human-computer interaction. These agents autonomo…
Futga: Towards Fine-grained Music Understanding through Temporally-enhanced Generative Augmentation
Junda Wu, Zachary Novack, Amit Namburi +5
Existing music captioning methods are limited to generating concise global descriptions of short music clips, which fail to capture fine-grained musical characteristics and time-aw…
Mitigating Hallucination in Fictional Character Role-Play
Nafis Sadeq, Zhouhang Xie, Byungkyu Kang +3
Role-playing has wide-ranging applications in customer support, embodied agents, and computational social science. The influence of parametric world knowledge of large language mod…
Reindex-Then-Adapt: Improving Large Language Models for Conversational Recommendation
Zhankui He, Zhouhang Xie, Harald Steck +4
Large language models (LLMs) are revolutionizing conversational recommender systems by adeptly indexing item content, understanding complex conversational contexts, and generating…
Few-shot Dialogue Strategy Learning for Motivational Interviewing via Inductive Reasoning
Zhouhang Xie, Bodhisattwa Prasad Majumder, Mengjie Zhao +4
We consider the task of building a dialogue system that can motivate users to adopt positive lifestyle changes: Motivational Interviewing. Addressing such a task requires a system…
RecWizard: A Toolkit for Conversational Recommendation with Modular, Portable Models and Interactive User Interface
Zeyuan Zhang, Tanmay Laud, Zihang He +5
We present a new Python toolkit called RecWizard for Conversational Recommender Systems (CRS). RecWizard offers support for development of models and interactive user interface, dr…
Large Language Models as Zero-Shot Conversational Recommenders
Zhankui He, Zhouhang Xie, Rahul Jha +6
In this paper, we present empirical studies on conversational recommendation tasks using representative large language models in a zero-shot setting with three primary contribution…
TCAB: A Large-Scale Text Classification Attack Benchmark
Kalyani Asthana, Zhouhang Xie, Wencong You +4
We introduce the Text Classification Attack Benchmark (TCAB), a dataset for analyzing, understanding, detecting, and labeling adversarial attacks against text classifiers. TCAB inc…
Factual and Informative Review Generation for Explainable Recommendation
Zhouhang Xie, Sameer Singh, Julian McAuley +1
Recent models can generate fluent and grammatical synthetic reviews while accurately predicting user ratings. The generated reviews, expressing users' estimated opinions towards re…
On Faithfulness and Coherence of Language Explanations for Recommendation Systems
Zhouhang Xie, Julian McAuley, Bodhisattwa Prasad Majumder
Reviews contain rich information about product characteristics and user interests and thus are commonly used to boost recommender system performance. Specifically, previous work sh…
Identifying Adversarial Attacks on Text Classifiers
Zhouhang Xie, Jonathan Brophy, Adam Noack +6
The landscape of adversarial attacks against text classifiers continues to grow, with new attacks developed every year and many of them available in standard toolkits, such as Text…