5 citations · 16 across the 17 of their papers we have counts for
18 papers
PolyWorkBench: Benchmarking LLM Agents for Cross-Lingual Long-Horizon Workflows
Hongliang Li, Yijin Liu, Zhiwei Zhang +5
While Large Language Model (LLM) agents excel at monolingual long-horizon planning and tool use, enterprise workflows inherently require processing multilingual resources across ex…
SED-SFT: Selectively Encouraging Diversity in Supervised Fine-Tuning
Yijie Chen, Yijin Liu, Fandong Meng
Supervised Fine-Tuning (SFT) followed by Reinforcement Learning (RL) has emerged as the standard post-training paradigm for large language models (LLMs). However, the conventional…
Warmup-Distill: Bridge the Distribution Mismatch between Teacher and Student before Knowledge Distillation
Zengkui Sun, Yijin Liu, Fandong Meng +3
The widespread deployment of Large Language Models (LLMs) is hindered by the high computational demands, making knowledge distillation (KD) crucial for developing compact smaller o…
Enhancing Cross-Tokenizer Knowledge Distillation with Contextual Dynamical Mapping
Yijie Chen, Yijin Liu, Fandong Meng +3
Knowledge Distillation (KD) has emerged as a prominent technique for model compression. However, conventional KD approaches primarily focus on homogeneous architectures with identi…
Beyond Binary Gender: Evaluating Gender-Inclusive Machine Translation with Ambiguous Attitude Words
Yijie Chen, Yijin Liu, Fandong Meng +3
Gender bias has been a focal point in the study of bias in machine translation and language models. Existing machine translation gender bias evaluations are primarily focused on ma…
Outdated Issue Aware Decoding for Reasoning Questions on Edited Knowledge
Zengkui Sun, Yijin Liu, Jiaan Wang +4
Recently, Knowledge Editing has received increasing attention, since it could update the specific knowledge from outdated ones in pretrained models without re-training. However, as…
LCS: A Language Converter Strategy for Zero-Shot Neural Machine Translation
Zengkui Sun, Yijin Liu, Fandong Meng +3
Multilingual neural machine translation models generally distinguish translation directions by the language tag (LT) in front of the source or target sentences. However, current LT…
Language Generation with Strictly Proper Scoring Rules
Chenze Shao, Fandong Meng, Yijin Liu +1
Language generation based on maximum likelihood estimation (MLE) has become the fundamental approach for text generation. Maximum likelihood estimation is typically performed by mi…
Comments as Natural Logic Pivots: Improve Code Generation via Comment Perspective
Yijie Chen, Yijin Liu, Fandong Meng +3
Code generation aims to understand the problem description and generate corresponding code snippets, where existing works generally decompose such complex tasks into intermediate s…
Accelerating Inference in Large Language Models with a Unified Layer Skipping Strategy
Yijin Liu, Fandong Meng, Jie Zhou
Recently, dynamic computation methods have shown notable acceleration for Large Language Models (LLMs) by skipping several layers of computations through elaborate heuristics or ad…
Scheduled Sampling Based on Decoding Steps for Neural Machine Translation
Yijin Liu, Fandong Meng, Yufeng Chen +2
Scheduled sampling is widely used to mitigate the exposure bias problem for neural machine translation. Its core motivation is to simulate the inference scene during training by re…
Confidence-Aware Scheduled Sampling for Neural Machine Translation
Yijin Liu, Fandong Meng, Yufeng Chen +2
Scheduled sampling is an effective method to alleviate the exposure bias problem of neural machine translation. It simulates the inference scene by randomly replacing ground-truth…
Prevent the Language Model from being Overconfident in Neural Machine Translation
Mengqi Miao, Fandong Meng, Yijin Liu +2
The Neural Machine Translation (NMT) model is essentially a joint language model conditioned on both the source sentence and partial translation. Therefore, the NMT model naturally…
Bilingual Mutual Information Based Adaptive Training for Neural Machine Translation
Yangyifan Xu, Yijin Liu, Fandong Meng +3
Recently, token-level adaptive training has achieved promising improvement in machine translation, where the cross-entropy loss function is adjusted by assigning different training…
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…
Faster Depth-Adaptive Transformers
Yijin Liu, Fandong Meng, Jie Zhou +2
Depth-adaptive neural networks can dynamically adjust depths according to the hardness of input words, and thus improve efficiency. The main challenge is how to measure such hardne…
Depth-Adaptive Graph Recurrent Network for Text Classification
Yijin Liu, Fandong Meng, Yufeng Chen +2
The Sentence-State LSTM (S-LSTM) is a powerful and high efficient graph recurrent network, which views words as nodes and performs layer-wise recurrent steps between them simultane…
CM-Net: A Novel Collaborative Memory Network for Spoken Language Understanding
Yijin Liu, Fandong Meng, Jinchao Zhang +3
Spoken Language Understanding (SLU) mainly involves two tasks, intent detection and slot filling, which are generally modeled jointly in existing works. However, most existing mode…