57 citations · 59 across the 4 of their papers we have counts for
4 papers
LLaDA2.1: Speeding Up Text Diffusion via Token Editing
Tiwei Bie, Maosong Cao, Xiang Cao +47
While LLaDA2.0 showcased the scaling potential of 100B-level block-diffusion models and their inherent parallelization, the delicate equilibrium between decoding speed and generati…
The Curse of CoT: On the Limitations of Chain-of-Thought in In-Context Learning
Tianshi Zheng, Yixiang Chen, Chengxi Li +7
Chain-of-Thought (CoT) prompting has been widely recognized for its ability to enhance reasoning capabilities in large language models (LLMs). However, our study reveals a surprisi…
OnlySportsLM: Optimizing Sports-Domain Language Models with SOTA Performance under Billion Parameters
Zexin Chen, Chengxi Li, Xiangyu Xie +1
This paper explores the potential of a small, domain-specific language model trained exclusively on sports-related data. We investigate whether extensive training data with special…
SentiPrompt: Sentiment Knowledge Enhanced Prompt-Tuning for Aspect-Based Sentiment Analysis
Chengxi Li, Feiyu Gao, Jiajun Bu +8
Aspect-based sentiment analysis (ABSA) is an emerging fine-grained sentiment analysis task that aims to extract aspects, classify corresponding sentiment polarities and find opinio…