5 citations · 17 across the 11 of their papers we have counts for
14 papers
Beyond Better Ideas: How Human-AI Collaboration Shapes Scientific Ideas and Human Perceptions
Qian Chen, Saatvik Pradhan, Janice Ahn +7
As AI increasingly becomes a collaborator in scientific research, its impact may extend beyond improving ideas to reshaping how they are evaluated, attributed, and perceived by hum…
PAPER2LLM++: Continual Self-Evolution of LLMs from Research Papers
Hongji Pu, Yilun Zhao, Wenpeng Yin
Research on LLMs continually uncovers model limitations, their causes, and potential solutions. Yet these human discoveries remain largely disconnected from model evolution: an LLM…
PatternFormer: Learning Multiple Solution Patterns in Reaction--Diffusion Systems
Zhipeng Chang, Wenpeng Yin, Wenrui Hao
Many nonlinear models across physics, chemistry, and biology exhibit multiple solutions for the same parameters, and capturing this entire solution set is essential for understandi…
CleanCodec: Efficient and Robust Speech Tokenization via Perceptually Guided Encoding
Eugene Kwek, Feng Liu, Rui Zhang +1
Neural audio codecs are a key component of speech processing pipelines, compressing audio into discrete tokens for downstream modeling. However, existing codecs struggle to balance…
Bridging the Know-Act Gap via Task-Level Autoregressive Reasoning
Jihyun Janice Ahn, Ryo Kamoi, Berk Atil +34
LLMs often generate seemingly valid answers to flawed or ill-posed inputs. This is not due to missing knowledge: under discriminative prompting, the same models can mostly identify…
Functionality-Oriented LLM Merging on the Fisher--Rao Manifold
Jiayu Wang, Zuojun Ye, Wenpeng Yin
Weight-space merging aims to combine multiple fine-tuned LLMs into a single model without retraining, yet most existing approaches remain fundamentally parameter-space heuristics.…
HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding?
Yusen Zhang, Wenliang Zheng, Aashrith Madasu +14
High-resolution image (HRI) understanding aims to process images with a large number of pixels, such as pathological images and agricultural aerial images, both of which can exceed…
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…
Prompt-Reverse Inconsistency: LLM Self-Inconsistency Beyond Generative Randomness and Prompt Paraphrasing
Jihyun Janice Ahn, Wenpeng Yin
While the inconsistency of LLMs is not a novel topic, prior research has predominantly addressed two types of generative inconsistencies: i) Randomness Inconsistency: running the s…
Direct-Inverse Prompting: Analyzing LLMs' Discriminative Capacity in Self-Improving Generation
Jihyun Janice Ahn, Ryo Kamoi, Lu Cheng +2
Mainstream LLM research has primarily focused on enhancing their generative capabilities. However, even the most advanced LLMs experience uncertainty in their outputs, often produc…
Can Prompt Modifiers Control Bias? A Comparative Analysis of Text-to-Image Generative Models
Philip Wootaek Shin, Jihyun Janice Ahn, Wenpeng Yin +2
It has been shown that many generative models inherit and amplify societal biases. To date, there is no uniform/systematic agreed standard to control/adjust for these biases. This…
Evaluating LLMs at Detecting Errors in LLM Responses
Ryo Kamoi, Sarkar Snigdha Sarathi Das, Renze Lou +12
With Large Language Models (LLMs) being widely used across various tasks, detecting errors in their responses is increasingly crucial. However, little research has been conducted o…
MT-Ranker: Reference-free machine translation evaluation by inter-system ranking
Ibraheem Muhammad Moosa, Rui Zhang, Wenpeng Yin
Traditionally, Machine Translation (MT) Evaluation has been treated as a regression problem -- producing an absolute translation-quality score. This approach has two limitations: i…
Unified Low-Resource Sequence Labeling by Sample-Aware Dynamic Sparse Finetuning
Sarkar Snigdha Sarathi Das, Ranran Haoran Zhang, Peng Shi +2
Unified Sequence Labeling that articulates different sequence labeling problems such as Named Entity Recognition, Relation Extraction, Semantic Role Labeling, etc. in a generalized…