13 citations · 55 across the 48 of their papers we have counts for
49 papers
Think Natively: Unlocking Multilingual Reasoning with Consistency-Enhanced Reinforcement Learning
Xue Zhang, Yunlong Liang, Fandong Meng +5
Large Reasoning Models (LRMs) have achieved remarkable performance on complex reasoning tasks by adopting the ``think-then-answer'' paradigm, which enhances both accuracy and inter…
CM-Align: Consistency-based Multilingual Alignment for Large Language Models
Xue Zhang, Yunlong Liang, Fandong Meng +4
Current large language models (LLMs) generally show a significant performance gap in alignment between English and other languages. To bridge this gap, existing research typically…
Less, but Better: Efficient Multilingual Expansion for LLMs via Layer-wise Mixture-of-Experts
Xue Zhang, Yunlong Liang, Fandong Meng +4
Continually expanding new languages for existing large language models (LLMs) is a promising yet challenging approach to building powerful multilingual LLMs. The biggest challenge…
A Dual-Space Framework for General Knowledge Distillation of Large Language Models
Xue Zhang, Songming Zhang, Yunlong Liang +4
Knowledge distillation (KD) is a promising solution to compress large language models (LLMs) by transferring their knowledge to smaller models. During this process, white-box KD me…
DMDTEval: An Evaluation and Analysis of LLMs on Disambiguation in Multi-domain Translation
Zhibo Man, Yuanmeng Chen, Yujie Zhang +1
Currently, Large Language Models (LLMs) have achieved remarkable results in machine translation. However, their performance in multi-domain translation (MDT) is less satisfactory,…
D2C: Unlocking the Potential of Continuous Autoregressive Image Generation with Discrete Tokens
Panpan Wang, Liqiang Niu, Fandong Meng +3
In the domain of image generation, latent-based generative models occupy a dominant status; however, these models rely heavily on image tokenizer. To meet modeling requirements, au…
AlignDistil: Token-Level Language Model Alignment as Adaptive Policy Distillation
Songming Zhang, Xue Zhang, Tong Zhang +3
In modern large language models (LLMs), LLM alignment is of crucial importance and is typically achieved through methods such as reinforcement learning from human feedback (RLHF) a…
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…
Textualized Agent-Style Reasoning for Complex Tasks by Multiple Round LLM Generation
Chen Liang, Zhifan Feng, Zihe Liu +4
Chain-of-thought prompting significantly boosts the reasoning ability of large language models but still faces three issues: hallucination problem, restricted interpretability, and…
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…
Dual-Space Knowledge Distillation for Large Language Models
Songming Zhang, Xue Zhang, Zengkui Sun +2
Knowledge distillation (KD) is known as a promising solution to compress large language models (LLMs) via transferring their knowledge to smaller models. During this process, white…
Multilingual Knowledge Editing with Language-Agnostic Factual Neurons
Xue Zhang, Yunlong Liang, Fandong Meng +4
Multilingual knowledge editing (MKE) aims to simultaneously update factual knowledge across multiple languages within large language models (LLMs). Previous research indicates that…
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…
Towards Faster k-Nearest-Neighbor Machine Translation
Xiangyu Shi, Yunlong Liang, Jinan Xu +1
Recent works have proven the effectiveness of k-nearest-neighbor machine translation(a.k.a kNN-MT) approaches to produce remarkable improvement in cross-domain translations. Howeve…
A Quality-based Syntactic Template Retriever for Syntactically-controlled Paraphrase Generation
Xue Zhang, Songming Zhang, Yunlong Liang +4
Existing syntactically-controlled paraphrase generation (SPG) models perform promisingly with human-annotated or well-chosen syntactic templates. However, the difficulty of obtaini…
Improving Translation Faithfulness of Large Language Models via Augmenting Instructions
Yijie Chen, Yijin Liu, Fandong Meng +3
Large Language Models (LLMs) present strong general capabilities, and a current compelling challenge is stimulating their specialized capabilities, such as machine translation, thr…
Unified Model Learning for Various Neural Machine Translation
Yunlong Liang, Fandong Meng, Jinan Xu +3
Existing neural machine translation (NMT) studies mainly focus on developing dataset-specific models based on data from different tasks (e.g., document translation and chat transla…
RC3: Regularized Contrastive Cross-lingual Cross-modal Pre-training
Chulun Zhou, Yunlong Liang, Fandong Meng +3
Multilingual vision-language (V&L) pre-training has achieved remarkable progress in learning universal representations across different modalities and languages. In spite of recent…
DTV: Dual Knowledge Distillation and Target-oriented Vision Modeling for Many-to-Many Multimodal Summarization
Yunlong Liang, Fandong Meng, Jiaan Wang +3
Many-to-many multimodal summarization (MS) task aims to generate summaries in any language with document inputs in any language and the corresponding image sequence, which esse…
Towards Understanding and Improving Knowledge Distillation for Neural Machine Translation
Songming Zhang, Yunlong Liang, Shuaibo Wang +4
Knowledge distillation (KD) is a promising technique for model compression in neural machine translation. However, where the knowledge hides in KD is still not clear, which may hin…
Is ChatGPT a Good NLG Evaluator? A Preliminary Study
Jiaan Wang, Yunlong Liang, Fandong Meng +6
Recently, the emergence of ChatGPT has attracted wide attention from the computational linguistics community. Many prior studies have shown that ChatGPT achieves remarkable perform…