4 citations · 11 across the 8 of their papers we have counts for
5 papers · 1 filter
WarriorCoder: Learning from Expert Battles to Augment Code Large Language Models
Huawen Feng, Pu Zhao, Qingfeng Sun +8
Despite recent progress achieved by code large language models (LLMs), their remarkable abilities are largely dependent on fine-tuning on the high-quality data, posing challenges f…
Self-Adaptive Reconstruction with Contrastive Learning for Unsupervised Sentence Embeddings
Junlong Liu, Xichen Shang, Huawen Feng +2
Unsupervised sentence embeddings task aims to convert sentences to semantic vector representations. Most previous works directly use the sentence representations derived from pretr…
Improving Factual Consistency of News Summarization by Contrastive Preference Optimization
Huawen Feng, Yan Fan, Xiong Liu +6
Despite the recent progress in news summarization made by large language models (LLMs), they often generate summaries that are factually inconsistent with original articles, known…
Preserving Commonsense Knowledge from Pre-trained Language Models via Causal Inference
Junhao Zheng, Qianli Ma, Shengjie Qiu +6
Fine-tuning has been proven to be a simple and effective technique to transfer the learned knowledge of Pre-trained Language Models (PLMs) to downstream tasks. However, vanilla fin…
Perturbation-based Self-supervised Attention for Attention Bias in Text Classification
Huawen Feng, Zhenxi Lin, Qianli Ma
In text classification, the traditional attention mechanisms usually focus too much on frequent words, and need extensive labeled data in order to learn. This paper proposes a pert…