2 citations · 4 across the 4 of their papers we have counts for
7 papers
See What LLMs Cannot Answer: A Self-Challenge Framework for Uncovering LLM Weaknesses
Yulong Chen, Yang Liu, Jianhao Yan +6
The impressive performance of Large Language Models (LLMs) has consistently surpassed numerous human-designed benchmarks, presenting new challenges in assessing the shortcomings of…
Selective Knowledge Distillation for Neural Machine Translation
Fusheng Wang, Jianhao Yan, Fandong Meng +1
Neural Machine Translation (NMT) models achieve state-of-the-art performance on many translation benchmarks. As an active research field in NMT, knowledge distillation is widely ap…
Multi-Unit Transformers for Neural Machine Translation
Jianhao Yan, Fandong Meng, Jie Zhou
Transformer models achieve remarkable success in Neural Machine Translation. Many efforts have been devoted to deepening the Transformer by stacking several units (i.e., a combinat…
A Sentiment-Controllable Topic-to-Essay Generator with Topic Knowledge Graph
Lin Qiao, Jianhao Yan, Fandong Meng +2
Generating a vivid, novel, and diverse essay with only several given topic words is a challenging task of natural language generation. In previous work, there are two problems left…
WeChat Neural Machine Translation Systems for WMT20
Fandong Meng, Jianhao Yan, Yijin Liu +8
We participate in the WMT 2020 shared news translation task on Chinese to English. Our system is based on the Transformer (Vaswani et al., 2017a) with effective variants and the DT…
Dual Past and Future for Neural Machine Translation
Jianhao Yan, Fandong Meng, Jie Zhou
Though remarkable successes have been achieved by Neural Machine Translation (NMT) in recent years, it still suffers from the inadequate-translation problem. Previous studies show…