11 citations · 13 across the 12 of their papers we have counts for
12 papers
Representation Robustness Under Executable Reasoning Constraints in Large Language Models for Mathematical Problem Solving
Sagnik Nath, Edith Aurora Graf, Liang Zhang +1
Large language models (LLMs) are increasingly evaluated on mathematical problem solving, yet prior work often treats representationally equivalent formulations as interchangeable a…
BEMEval-Doc2Schema: Benchmarking Large Language Models for Structured Data Extraction in Building Energy Modeling
Yiyuan Jia, Xiaoqin Fu, Liang Zhang
Recent advances in foundation models, including large language models (LLMs), have created new opportunities to automate building energy modeling (BEM). However, systematic evaluat…
A State-Transition Framework for Efficient LLM Reasoning
Liang Zhang, Yu Zhao, Longyue Wang +4
While Long Chain-of-Thought (CoT) reasoning significantly improves Large Language Models (LLMs) performance on complex reasoning tasks, the substantial computational and memory cos…
Ask, Answer, and Detect: Role-Playing LLMs for Personality Detection with Question-Conditioned Mixture-of-Experts
Yifan Lyu, Liang Zhang
Understanding human personality is important for personalized recommendation and mental health assessment. Existing personality detection methods typically encode social media post…
Unilaw-R1: A Large Language Model for Legal Reasoning with Reinforcement Learning and Iterative Inference
Hua Cai, Shuang Zhao, Liang Zhang +5
Reasoning-focused large language models (LLMs) are rapidly evolving across various domains, yet their capabilities in handling complex legal problems remains underexplored. In this…
Mathematical Computation and Reasoning Errors by Large Language Models
Liang Zhang, Edith Aurora Graf
Large Language Models (LLMs) are increasingly utilized in AI-driven educational instruction and assessment, particularly within mathematics education. The capability of LLMs to gen…
Improved LLM Agents for Financial Document Question Answering
Nelvin Tan, Zian Seng, Liang Zhang +3
Large language models (LLMs) have shown impressive capabilities on numerous natural language processing tasks. However, LLMs still struggle with numerical question answering for fi…
How Is LLM Reasoning Distracted by Irrelevant Context? An Analysis Using a Controlled Benchmark
Minglai Yang, Ethan Huang, Liang Zhang +3
We introduce Grade School Math with Distracting Context (GSM-DC), a synthetic benchmark to evaluate Large Language Models' (LLMs) reasoning robustness against systematically contro…
Optimizing Case-Based Reasoning System for Functional Test Script Generation with Large Language Models
Siyuan Guo, Huiwu Liu, Xiaolong Chen +6
In this work, we explore the potential of large language models (LLMs) for generating functional test scripts, which necessitates understanding the dynamically evolving code struct…
A Large Language Model-Driven Reward Design Framework via Dynamic Feedback for Reinforcement Learning
Shengjie Sun, Runze Liu, Jiafei Lyu +3
Large Language Models (LLMs) have shown significant potential in designing reward functions for Reinforcement Learning (RL) tasks. However, obtaining high-quality reward code often…
Enhancing Large Language Model Performance To Answer Questions and Extract Information More Accurately
Liang Zhang, Katherine Jijo, Spurthi Setty +4
Large Language Models (LLMs) generate responses to questions; however, their effectiveness is often hindered by sub-optimal quality of answers and occasional failures to provide ac…
Improving Classification Performance With Human Feedback: Label a few, we label the rest
Natan Vidra, Thomas Clifford, Katherine Jijo +2
In the realm of artificial intelligence, where a vast majority of data is unstructured, obtaining substantial amounts of labeled data to train supervised machine learning models po…