1 citations · 2 across the 7 of their papers we have counts for
10 papers
TeachGrade: Training-Free Short Answer Scoring with Teacher Evidence
Jinhao Yan, Zhihong Shen, Shengjie Wang +4
Multi-subject, multi-step scoring requires consistent partial credit across grading conventions. We introduce TeachGrade, a training-free framework using teacher-graded examples. T…
DIRECT: Direct Decoding for Efficient and Aligned Sequence Labeling with Large Language Models
Yilei Wang, Jiaxin Gan, Kexuan Zhang +3
Sequence labeling is a fine-grained information extraction task, yet existing large language model-based approaches suffer from insufficient domain alignment and low inference effi…
DataFlex: A Unified Framework for Data-Centric Dynamic Training of Large Language Models
Hao Liang, Zhengyang Zhao, Meiyi Qiang +22
Data-centric training has emerged as a promising direction for improving large language models (LLMs) by optimizing not only model parameters but also the selection, composition, a…
pQuant: Towards Effective Low-Bit Language Models via Decoupled Linear Quantization-Aware Training
Wenzheng Zhang, Bingzheng Liu, Yang Hu +3
Quantization-Aware Training from scratch has emerged as a promising approach for building efficient large language models (LLMs) with extremely low-bit weights (sub 2-bit), which c…
ChartVerse: Scaling Chart Reasoning via Reliable Programmatic Synthesis from Scratch
Zheng Liu, Honglin Lin, Chonghan Qin +13
Chart reasoning is a critical capability for Vision Language Models (VLMs). However, the development of open-source models is severely hindered by the lack of high-quality training…
Heterogeneous Adaptive Policy Optimization: Tailoring Optimization to Every Token's Nature
Zheng Liu, Mengjie Liu, Siwei Wen +4
Using entropy as a measure of heterogeneity to guide optimization has emerged as a crucial research direction in Reinforcement Learning for LLMs. However, existing methods typicall…
PilotRL: Training Language Model Agents via Global Planning-Guided Progressive Reinforcement Learning
Keer Lu, Chong Chen, Bin Cui +2
Large Language Models (LLMs) have shown remarkable advancements in tackling agent-oriented tasks. Despite their potential, existing work faces challenges when deploying LLMs in age…
Modeling All-Atom Glycan Structures via Hierarchical Message Passing and Multi-Scale Pre-training
Minghao Xu, Jiaze Song, Keming Wu +3
Understanding the various properties of glycans with machine learning has shown some preliminary promise. However, previous methods mainly focused on modeling the backbone structur…
SAS-Bench: A Fine-Grained Benchmark for Evaluating Short Answer Scoring with Large Language Models
Peichao Lai, Kexuan Zhang, Yi Lin +8
Subjective Answer Grading (SAG) plays a crucial role in education, standardized testing, and automated assessment systems, particularly for evaluating short-form responses in Short…
Enhancing Unsupervised Sentence Embeddings via Knowledge-Driven Data Augmentation and Gaussian-Decayed Contrastive Learning
Peichao Lai, Zhengfeng Zhang, Wentao Zhang +2
Recently, using large language models (LLMs) for data augmentation has led to considerable improvements in unsupervised sentence embedding models. However, existing methods encount…