4 citations · 5 across the 11 of their papers we have counts for
11 papers
CoLT: Reasoning with Chain of Latent Tool Calls
Fangwei Zhu, Zhifang Sui
Chain-of-Thought (CoT) is a critical technique in enhancing the reasoning ability of Large Language Models (LLMs), and latent reasoning methods have been proposed to accelerate the…
RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection
Yixin Yang, Qingxiu Dong, Linli Yao +2
Data selection for instruction tuning is crucial for improving the performance of large language models (LLMs) while reducing training costs. In this paper, we propose Refined Cont…
Chain-of-Thought Tokens are Computer Program Variables
Fangwei Zhu, Peiyi Wang, Zhifang Sui
Chain-of-thoughts (CoT) requires large language models (LLMs) to generate intermediate steps before reaching the final answer, and has been proven effective to help LLMs solve comp…
Chip-Tuning: Classify Before Language Models Say
Fangwei Zhu, Dian Li, Jiajun Huang +3
The rapid development in the performance of large language models (LLMs) is accompanied by the escalation of model size, leading to the increasing cost of model training and infere…
LLMAEL: Large Language Models are Good Context Augmenters for Entity Linking
Amy Xin, Yunjia Qi, Zijun Yao +5
Specialized entity linking (EL) models are well-trained at mapping mentions to unique knowledge base (KB) entities according to a given context. However, specialized EL models stru…
CoUDA: Coherence Evaluation via Unified Data Augmentation
Dawei Zhu, Wenhao Wu, Yifan Song +3
Coherence evaluation aims to assess the organization and structure of a discourse, which remains challenging even in the era of large language models. Due to the scarcity of annota…
Reducing Hallucinations in Entity Abstract Summarization with Facts-Template Decomposition
Fangwei Zhu, Peiyi Wang, Zhifang Sui
Entity abstract summarization aims to generate a coherent description of a given entity based on a set of relevant Internet documents. Pretrained language models (PLMs) have achiev…
Language Models Encode the Value of Numbers Linearly
Fangwei Zhu, Damai Dai, Zhifang Sui
Large language models (LLMs) have exhibited impressive competence in various tasks, but their internal mechanisms on mathematical problems are still under-explored. In this paper,…
Learn to Not Link: Exploring NIL Prediction in Entity Linking
Fangwei Zhu, Jifan Yu, Hailong Jin +3
Entity linking models have achieved significant success via utilizing pretrained language models to capture semantic features. However, the NIL prediction problem, which aims to id…
A Roadmap for Big Model
Sha Yuan, Hanyu Zhao, Shuai Zhao +97
With the rapid development of deep learning, training Big Models (BMs) for multiple downstream tasks becomes a popular paradigm. Researchers have achieved various outcomes in the c…
TWAG: A Topic-Guided Wikipedia Abstract Generator
Fangwei Zhu, Shangqing Tu, Jiaxin Shi +3
Wikipedia abstract generation aims to distill a Wikipedia abstract from web sources and has met significant success by adopting multi-document summarization techniques. However, pr…