4 citations · 11 across the 16 of their papers we have counts for
18 papers
TSUBASA: Improving Long-Horizon Personalization via Evolving Memory and Self-Learning with Context Distillation
Xinliang Frederick Zhang, Lu Wang
Personalized large language models (PLLMs) have garnered significant attention for their ability to align outputs with individual's needs and preferences. However, they still strug…
Do LLMs Really Need 10+ Thoughts for "Find the Time 1000 Days Later"? Towards Structural Understanding of LLM Overthinking
Xinliang Frederick Zhang, Anhad Mohananey, Alexandra Chronopoulou +5
Models employing long chain-of-thought (CoT) reasoning have shown superior performance on complex reasoning tasks. Yet, this capability introduces a critical and often overlooked i…
LiveOIBench: Can Large Language Models Outperform Human Contestants in Informatics Olympiads?
Kaijian Zou, Aaron Xiong, Yunxiang Zhang +6
Competitive programming problems are increasingly used to evaluate the coding capabilities of large language models (LLMs) due to their complexity and ease of verification. Yet, cu…
Logit Arithmetic Elicits Long Reasoning Capabilities Without Training
Yunxiang Zhang, Muhammad Khalifa, Lechen Zhang +5
Large reasoning models exhibit long chain-of-thought reasoning with complex strategies such as backtracking and self-verification. Yet, these capabilities typically require resourc…
Logit Arithmetic Elicits Long Reasoning Capabilities Without Training
Yunxiang Zhang, Muhammad Khalifa, Lechen Zhang +5
Large reasoning models (LRMs) can do complex reasoning via long chain-of-thought (CoT) involving cognitive strategies such as backtracking and self-correction. Recent studies sugge…
PRIME: Large Language Model Personalization with Cognitive Dual-Memory and Personalized Thought Process
Xinliang Frederick Zhang, Nick Beauchamp, Lu Wang
Large language model (LLM) personalization aims to align model outputs with individuals' unique preferences and opinions. While recent efforts have implemented various personalizat…
Narrative-of-Thought: Improving Temporal Reasoning of Large Language Models via Recounted Narratives
Xinliang Frederick Zhang, Nick Beauchamp, Lu Wang
Reasoning about time and temporal relations is an integral aspect of human cognition, essential for perceiving the world and navigating our experiences. Though large language model…
ULTRA: Unleash LLMs' Potential for Event Argument Extraction through Hierarchical Modeling and Pair-wise Self-Refinement
Xinliang Frederick Zhang, Carter Blum, Temma Choji +2
Structural extraction of events within discourse is critical since it avails a deeper understanding of communication patterns and behavior trends. Event argument extraction (EAE),…
MOKA: Moral Knowledge Augmentation for Moral Event Extraction
Xinliang Frederick Zhang, Winston Wu, Nick Beauchamp +1
News media often strive to minimize explicit moral language in news articles, yet most articles are dense with moral values as expressed through the reported events themselves. How…
All Things Considered: Detecting Partisan Events from News Media with Cross-Article Comparison
Yujian Liu, Xinliang Frederick Zhang, Kaijian Zou +3
Public opinion is shaped by the information news media provide, and that information in turn may be shaped by the ideological preferences of media outlets. But while much attention…
Crossing the Aisle: Unveiling Partisan and Counter-Partisan Events in News Reporting
Kaijian Zou, Xinliang Frederick Zhang, Winston Wu +2
News media is expected to uphold unbiased reporting. Yet they may still affect public opinion by selectively including or omitting events that support or contradict their ideologic…
You Are What You Annotate: Towards Better Models through Annotator Representations
Naihao Deng, Xinliang Frederick Zhang, Siyang Liu +3
Annotator disagreement is ubiquitous in natural language processing (NLP) tasks. There are multiple reasons for such disagreements, including the subjectivity of the task, difficul…
Late Fusion with Triplet Margin Objective for Multimodal Ideology Prediction and Analysis
Changyuan Qiu, Winston Wu, Xinliang Frederick Zhang +1
Prior work on ideology prediction has largely focused on single modalities, i.e., text or images. In this work, we introduce the task of multimodal ideology prediction, where a mod…
Generative Entity-to-Entity Stance Detection with Knowledge Graph Augmentation
Xinliang Frederick Zhang, Nick Beauchamp, Lu Wang
Stance detection is typically framed as predicting the sentiment in a given text towards a target entity. However, this setup overlooks the importance of the source entity, i.e., w…
POLITICS: Pretraining with Same-story Article Comparison for Ideology Prediction and Stance Detection
Yujian Liu, Xinliang Frederick Zhang, David Wegsman +2
Ideology is at the core of political science research. Yet, there still does not exist general-purpose tools to characterize and predict ideology across different genres of text. T…
SimSR: Simple Distance-based State Representation for Deep Reinforcement Learning
Hongyu Zang, Xin Li, Mingzhong Wang
This work explores how to learn robust and generalizable state representation from image-based observations with deep reinforcement learning methods. Addressing the computational c…
CliniQG4QA: Generating Diverse Questions for Domain Adaptation of Clinical Question Answering
Xiang Yue, Xinliang Frederick Zhang, Ziyu Yao +2
Clinical question answering (QA) aims to automatically answer questions from medical professionals based on clinical texts. Studies show that neural QA models trained on one corpus…
COUGH: A Challenge Dataset and Models for COVID-19 FAQ Retrieval
Xinliang Frederick Zhang, Heming Sun, Xiang Yue +2
We present a large, challenging dataset, COUGH, for COVID-19 FAQ retrieval. Similar to a standard FAQ dataset, COUGH consists of three parts: FAQ Bank, Query Bank and Relevance Set…