14 papers
Beyond ID Embeddings: Process-Grounded Language Modeling for Cognitive Diagnosis
Minghang Liu, Yuanzhuo Wang, Qiang Qiu +2
Cognitive Diagnosis Models (CDMs) play a pivotal role in personalized online learning. Traditional CDMs rely on discrete, ID-based embeddings to represent students, exercises, and…
Less Is Personal: Learning Minimal Sufficient User Profiles for Personalized Language Models
Minghang Liu, Qiang Qiu, Yuanzhuo Wang +2
Retrieval-augmented personalization enables large language models to produce more accurate and preference-aligned outputs using relevant records retrieved from user histories. Pers…
RECAP: Relation Evidence Calibration for Detecting Spatial Relation Hallucinations in Vision-Language Models
Feixiang Liu, Qiang Qiu, Qingyang Li +1
Vision-language models can answer spatial relation questions confidently even when the image supports an incompatible relation. We formulate relation-grounded selective prediction:…
SEER: A Self-Grounded Evidence Interface for Controlled Spatial Relation Classification
Feixiang Liu, Likun Wang, Qiang Qiu +3
Spatial relation questions require a model to identify the queried subject and object before comparing their layout. Yet a VLM can recognize both entities and still answer from the…
Beyond Accuracy: Auditing Spatial Provenance in Visual Token Pruning for OCR-Critical MLLM Inference
Feixiang Liu, Qiang Qiu, Hao Zhang +1
Visual-token pruning is usually judged by answer quality at a fixed retention budget. For text-rich multimodal large language models (MLLMs), this protocol can miss a distinct fail…
Witness Evidence Portfolios: Single-Prefill Risk Detection for Closed Multimodal Answers
Fexiang Liu, Shiye Wang, Qiang Qiu +1
Reliable deployment of multimodal large language models (MLLMs) requires deciding whether a confident visual answer should be trusted, reviewed, or routed to a stronger system. Con…
Visual Credit Audit for Multimodal Spatial Reasoning
Feixiang Liu, Qiang Qiu, Lanbo Sun +3
Closed yes/no spatial benchmarks can reward a correct answer even when the image adds little support beyond no-image contexts. Under a fixed forced-choice interface, Visual Credit…
Steering Diffusion Models via Class-Contrastive Influence for Few-Shot Medical Classification
Jeeyung Kim, Erfan Esmaeili, Qiang Qiu
When labeled data are scarce, off-the-shelf diffusion models can augment training sets for few-shot medical image classification, but not all generated samples are equally useful f…
From Classification to Ranking: Enhancing LLM Reasoning Capabilities for MBTI Personality Detection
Yuan Cao, Feixiang Liu, Xinyue Wang +4
Personality detection aims to measure an individual's corresponding personality traits through their social media posts. The advancements in Large Language Models (LLMs) offer nove…
Text Embedding is Not All You Need: Attention Control for Text-to-Image Semantic Alignment with Text Self-Attention Maps
Jeeyung Kim, Erfan Esmaeili, Qiang Qiu
In text-to-image diffusion models, the cross-attention map of each text token indicates the specific image regions attended. Comparing these maps of syntactically related tokens pr…
Conditional Text-to-Image Generation with Reference Guidance
Taewook Kim, Ze Wang, Zhengyuan Yang +4
Text-to-image diffusion models have demonstrated tremendous success in synthesizing visually stunning images given textual instructions. Despite remarkable progress in creating hig…
Tuning Timestep-Distilled Diffusion Model Using Pairwise Sample Optimization
Zichen Miao, Zhengyuan Yang, Kevin Lin +4
Recent advancements in timestep-distilled diffusion models have enabled high-quality image generation that rivals non-distilled multi-step models, but with significantly fewer infe…
Constructing Concept-based Models to Mitigate Spurious Correlations with Minimal Human Effort
Jeeyung Kim, Ze Wang, Qiang Qiu
Enhancing model interpretability can address spurious correlations by revealing how models draw their predictions. Concept Bottleneck Models (CBMs) can provide a principled way of…
Model-Agnostic Human Preference Inversion in Diffusion Models
Jeeyung Kim, Ze Wang, Qiang Qiu
Efficient text-to-image generation remains a challenging task due to the high computational costs associated with the multi-step sampling in diffusion models. Although distillation…