4 citations · 4 across the 8 of their papers we have counts for
8 papers
Planner-Guided Recursive Exploration for Diffusion Language Model Decoding
Yingqian Cui, Wei Deng, Lantao Mei +4
Masked diffusion language models (DLMs) enable parallel text generation by iteratively refining masked tokens, offering a promising alternative to autoregressive decoding. Recent s…
"**Important** You should give me full credits!": Exploring Prompt Injection Attacks on LLM-Based Automatic Grading Systems
Hang Li, Fedor Filippov, Yuping Lin +6
The emergence of large language models (LLMs) has significantly accelerated recent research on LLM-based automatic grading (AG) systems. Benefiting from the strong instruction-foll…
More Efficient LLM Reranking with Whole-Pool, Setwise, Long-Context Language Models
Hang Li, Chuting Yu, Teerapong Leelanupab +2
LLM-based re-rankers produce rankings through repeated local comparisons (listwise, pairwise or pointwise), requiring many sequential model calls. We study how long-context LLMs ca…
Reasoning by Exploration: A Unified Approach to Retrieval and Generation over Graphs
Haoyu Han, Kai Guo, Harry Shomer +5
Reasoning over structured graphs remains a fundamental challenge for Large Language Models (LLMs), particularly when scaling to large graphs. Existing approaches typically follow t…
Enhancing LLM-Based Short Answer Grading with Retrieval-Augmented Generation
Yucheng Chu, Peng He, Hang Li +6
Short answer assessment is a vital component of science education, allowing evaluation of students' complex three-dimensional understanding. Large language models (LLMs) that posse…
LLM-based Automated Grading with Human-in-the-Loop
Yucheng Chu, Hang Li, Kaiqi Yang +2
The rise of artificial intelligence (AI) technologies, particularly large language models (LLMs), has brought significant advancements to the field of education. Among various appl…
A LLM-Powered Automatic Grading Framework with Human-Level Guidelines Optimization
Yucheng Chu, Hang Li, Kaiqi Yang +4
Open-ended short-answer questions (SAGs) have been widely recognized as a powerful tool for providing deeper insights into learners' responses in the context of learning analytics…
Sub-graph Based Diffusion Model for Link Prediction
Hang Li, Wei Jin, Geri Skenderi +4
Denoising Diffusion Probabilistic Models (DDPMs) represent a contemporary class of generative models with exceptional qualities in both synthesis and maximizing the data likelihood…