8 citations · 19 across the 10 of their papers we have counts for
10 papers
IndustryBench: Probing the Industrial Knowledge Boundaries of LLMs
Songlin Bai, Xintong Wang, Linlin Yu +12
In industrial procurement, an LLM answer is useful only if it survives a standards check: recommended material must match operating condition, every parameter must respect a regula…
Thinking Broad, Acting Fast: Latent Reasoning Distillation from Multi-Perspective Chain-of-Thought for E-Commerce Relevance
Baopu Qiu, Hao Chen, Yuanrong Wu +4
Effective relevance modeling is crucial for e-commerce search, as it aligns search results with user intent and enhances customer experience. Recent work has leveraged large langua…
Self-Evolution Knowledge Distillation for LLM-based Machine Translation
Yuncheng Song, Liang Ding, Changtong Zan +1
Knowledge distillation (KD) has shown great promise in transferring knowledge from larger teacher models to smaller student models. However, existing KD strategies for large langua…
Building Accurate Translation-Tailored LLMs with Language Aware Instruction Tuning
Changtong Zan, Liang Ding, Li Shen +3
Translation-tailored Large language models (LLMs) exhibit remarkable translation capabilities, even competing with supervised-trained commercial translation systems. However, off-t…
Unlikelihood Tuning on Negative Samples Amazingly Improves Zero-Shot Translation
Changtong Zan, Liang Ding, Li Shen +4
Zero-shot translation (ZST), which is generally based on a multilingual neural machine translation model, aims to translate between unseen language pairs in training data. The comm…
Unsupervised Dense Retrieval with Relevance-Aware Contrastive Pre-Training
Yibin Lei, Liang Ding, Yu Cao +3
Dense retrievers have achieved impressive performance, but their demand for abundant training data limits their application scenarios. Contrastive pre-training, which constructs ps…
Prompt-Learning for Cross-Lingual Relation Extraction
Chiaming Hsu, Changtong Zan, Liang Ding +5
Relation Extraction (RE) is a crucial task in Information Extraction, which entails predicting relationships between entities within a given sentence. However, extending pre-traine…
On the Complementarity between Pre-Training and Random-Initialization for Resource-Rich Machine Translation
Changtong Zan, Liang Ding, Li Shen +3
Pre-Training (PT) of text representations has been successfully applied to low-resource Neural Machine Translation (NMT). However, it usually fails to achieve notable gains (someti…
Vega-MT: The JD Explore Academy Translation System for WMT22
Changtong Zan, Keqin Peng, Liang Ding +9
We describe the JD Explore Academy's submission of the WMT 2022 shared general translation task. We participated in all high-resource tracks and one medium-resource track, includin…
Bridging Cross-Lingual Gaps During Leveraging the Multilingual Sequence-to-Sequence Pretraining for Text Generation and Understanding
Changtong Zan, Liang Ding, Li Shen +3
For multilingual sequence-to-sequence pretrained language models (multilingual Seq2Seq PLMs), e.g. mBART, the self-supervised pretraining task is trained on a wide range of monolin…