29 citations · 37 across the 45 of their papers we have counts for
6 papers · 2 filters
Analyzing the Effects of Supervised Fine-Tuning on Model Knowledge from Token and Parameter Levels
Junjie Ye, Yuming Yang, Yang Nan +7
Large language models (LLMs) acquire substantial world knowledge during pre-training, which is further shaped by post-training techniques such as supervised fine-tuning (SFT). Howe…
Game-RL: Synthesizing Multimodal Verifiable Game Data to Boost VLMs' General Reasoning
Jingqi Tong, Jixin Tang, Hangcheng Li +21
Vision-language reinforcement learning (RL) has primarily focused on narrow domains (e.g. geometry or chart reasoning). This leaves broader training scenarios and resources underex…
Effective Length Extrapolation via Dimension-Wise Positional Embeddings Manipulation
Yi Lu, Wanxu Zhao, Xin Zhou +9
Large Language Models (LLMs) often struggle to process and generate coherent context when the number of input tokens exceeds the pre-trained length. Recent advancements in long-con…
Machine-assisted writing evaluation: Exploring pre-trained language models in analyzing argumentative moves
Wenjuan Qin, Weiran Wang, Yuming Yang +1
The study investigates the efficacy of pre-trained language models (PLMs) in analyzing argumentative moves in a longitudinal learner corpus. Prior studies on argumentative moves of…
CritiQ: Mining Data Quality Criteria from Human Preferences
Honglin Guo, Kai Lv, Qipeng Guo +8
Language model heavily depends on high-quality data for optimal performance. Existing approaches rely on manually designed heuristics, the perplexity of existing models, training c…
Towards Economical Inference: Enabling DeepSeek's Multi-Head Latent Attention in Any Transformer-based LLMs
Tao Ji, Bin Guo, Yuanbin Wu +6
Multi-head Latent Attention (MLA) is an innovative architecture proposed by DeepSeek, designed to ensure efficient and economical inference by significantly compressing the Key-Val…