2 citations · 3 across the 2 of their papers we have counts for
5 papers
CLUE: Non-parametric Verification from Experience via Hidden-State Clustering
Zhenwen Liang, Ruosen Li, Yujun Zhou +5
Assessing the quality of Large Language Model (LLM) outputs presents a critical challenge. Previous methods either rely on text-level information (e.g., reward models, majority vot…
SaSR-Net: Source-Aware Semantic Representation Network for Enhancing Audio-Visual Question Answering
Tianyu Yang, Yiyang Nan, Lisen Dai +3
Audio-Visual Question Answering (AVQA) is a challenging task that involves answering questions based on both auditory and visual information in videos. A significant challenge is i…
Manipulating Predictions over Discrete Inputs in Machine Teaching
Xiaodong Wu, Yufei Han, Hayssam Dahrouj +3
Machine teaching often involves the creation of an optimal (typically minimal) dataset to help a model (referred to as the `student') achieve specific goals given by a teacher. Whi…
MinT: Boosting Generalization in Mathematical Reasoning via Multi-View Fine-Tuning
Zhenwen Liang, Dian Yu, Xiaoman Pan +4
Reasoning in mathematical domains remains a significant challenge for relatively small language models (LMs). Many current methods focus on specializing LMs in mathematical reasoni…
Let GPT be a Math Tutor: Teaching Math Word Problem Solvers with Customized Exercise Generation
Zhenwen Liang, Wenhao Yu, Tanmay Rajpurohit +3
In this paper, we present a novel approach for distilling math word problem solving capabilities from large language models (LLMs) into smaller, more efficient student models. Our…