40 citations · 100 across the 68 of their papers we have counts for
21 papers · 1 filter
FrontierCS: Evolving Challenges for Evolving Intelligence
Qiuyang Mang, Wenhao Chai, Zhifei Li +48
We introduce FrontierCS, a benchmark of 156 open-ended problems across diverse areas of computer science, designed and reviewed by experts, including CS PhDs and top-tier competiti…
QuCo-RAG: Quantifying Uncertainty from the Pre-training Corpus for Dynamic Retrieval-Augmented Generation
Dehai Min, Kailin Zhang, Tongtong Wu +1
Dynamic Retrieval-Augmented Generation adaptively determines when to retrieve during generation to mitigate hallucinations in large language models (LLMs). However, existing method…
Revisiting NLI: Towards Cost-Effective and Human-Aligned Metrics for Evaluating LLMs in Question Answering
Sai Shridhar Balamurali, Lu Cheng
Evaluating answers from state-of-the-art large language models (LLMs) is challenging: lexical metrics miss semantic nuances, whereas "LLM-as-Judge" scoring is computationally expen…
InsurAgent: A Large Language Model-Empowered Agent for Simulating Individual Behavior in Purchasing Flood Insurance
Ziheng Geng, Jiachen Liu, Ran Cao +3
Flood insurance is an effective strategy for individuals to mitigate disaster-related losses. However, participation rates among at-risk populations in the United States remain str…
Early Risk Prediction with Temporally and Contextually Grounded Clinical Language Processing
Rochana Chaturvedi, Yue Zhou, Andrew D. Boyd +5
Clinical notes in Electronic Health Records (EHRs) capture rich temporal information on events, clinician reasoning, and lifestyle factors often missing from structured data. Lever…
MVI-Bench: A Comprehensive Benchmark for Evaluating Robustness to Misleading Visual Inputs in LVLMs
Huiyi Chen, Jiawei Peng, Dehai Min +5
Evaluating the robustness of Large Vision-Language Models (LVLMs) is essential for their continued development and responsible deployment in real-world applications. However, exist…
Robust Uncertainty Quantification for Self-Evolving Large Language Models via Continual Domain Pretraining
Xiaofan Zhou, Lu Cheng
Continual Learning (CL) is essential for enabling self-evolving large language models (LLMs) to adapt and remain effective amid rapid knowledge growth. Yet, despite its importance,…
A Lightweight Large Language Model-Based Multi-Agent System for 2D Frame Structural Analysis
Ziheng Geng, Jiachen Liu, Ran Cao +3
Large language models (LLMs) have recently been used to empower autonomous agents in engineering, significantly improving automation and efficiency in labor-intensive workflows. Ho…
What Shapes a Creative Machine Mind? Comprehensively Benchmarking Creativity in Foundation Models
Zicong He, Boxuan Zhang, Weihao Liu +2
The meteoric rise of foundation models (FMs) has expanded their capabilities far beyond conventional tasks. Creativity, long regarded as a hallmark of human intelligence and a driv…
Smart Trial: Evaluating the Use of Large Language Models for Recruiting Clinical Trial Participants via Social Media
Xiaofan Zhou, Zisu Wang, Janice Krieger +2
Clinical trials (CT) are essential for advancing medical research and treatment, yet efficiently recruiting eligible participants -- each of whom must meet complex eligibility crit…
SoM-1K: A Thousand-Problem Benchmark Dataset for Strength of Materials
Qixin Wan, Zilong Wang, Jingwen Zhou +5
Foundation models have shown remarkable capabilities in various domains, but their performance on complex, multimodal engineering problems remains largely unexplored. We introduce…
REHEARSE: Experiential Rehearsal for Verbal Confidence Calibration in Large Language Models
Ke Fang, Tianyi Zhao, Qianwen Wang +1
Large language models (LLMs) often express verbal confidence that is poorly aligned with actual correctness, limiting their reliability in safety-critical applications. Existing pr…
A Large Language Model-Empowered Agent for Reliable and Robust Structural Analysis
Jiachen Liu, Ziheng Geng, Ran Cao +3
Large language models (LLMs) have exhibited remarkable capabilities across diverse open-domain tasks, yet their application in specialized domains such as civil engineering remains…
CP-Router: An Uncertainty-Aware Router Between LLM and LRM
Jiayuan Su, Fulin Lin, Zhaopeng Feng +7
Recent advances in Large Reasoning Models (LRMs) have significantly improved long-chain reasoning capabilities over Large Language Models (LLMs). However, LRMs often produce unnece…
Beyond Accuracy: The Role of Calibration in Self-Improving Large Language Models
Liangjie Huang, Dawei Li, Huan Liu +1
Large Language Models (LLMs) have demonstrated remarkable self-improvement capabilities, whereby models iteratively revise their outputs through self-generated feedback. While this…
Accelerating Causal Network Discovery of Alzheimer Disease Biomarkers via Scientific Literature-based Retrieval Augmented Generation
Xiaofan Zhou, Liangjie Huang, Pinyang Cheng +4
The causal relationships between biomarkers are essential for disease diagnosis and medical treatment planning. One notable application is Alzheimer's disease (AD) diagnosis, where…
Shakespearean Sparks: The Dance of Hallucination and Creativity in LLMs' Decoding Layers
Zicong He, Boxuan Zhang, Lu Cheng
Large language models (LLMs) are known to hallucinate, a phenomenon often linked to creativity. While previous research has primarily explored this connection through theoretical o…
DBR: Divergence-Based Regularization for Debiasing Natural Language Understanding Models
Zihao Li, Ruixiang Tang, Lu Cheng +3
Pre-trained language models (PLMs) have achieved impressive results on various natural language processing tasks. However, recent research has revealed that these models often rely…
Understanding the Uncertainty of LLM Explanations: A Perspective Based on Reasoning Topology
Longchao Da, Xiaoou Liu, Jiaxin Dai +3
Understanding the uncertainty in large language model (LLM) explanations is important for evaluating their faithfulness and reasoning consistency, and thus provides insights into t…
COPU: Conformal Prediction for Uncertainty Quantification in Natural Language Generation
Sean Wang, Yicheng Jiang, Yuxin Tang +2
Uncertainty Quantification (UQ) for Natural Language Generation (NLG) is crucial for assessing the performance of Large Language Models (LLMs), as it reveals confidence in predicti…
Large Vision-Language Model Alignment and Misalignment: A Survey Through the Lens of Explainability
Dong Shu, Haiyan Zhao, Jingyu Hu +4
Large Vision-Language Models (LVLMs) have demonstrated remarkable capabilities in processing both visual and textual information. However, the critical challenge of alignment betwe…