16 citations · 58 across the 35 of their papers we have counts for
37 papers
Rewrite What Matters: Adaptive Multilingual Query Rewriting for Reasoning via Agentic Reinforcement Learning
Rui Qi, Yufeng Chen, Yunlong Liang +6
In multilingual scenarios, queries with equivalent semantics but in different languages could guide the model into different reasoning trajectories, leading to performance disparit…
Self-Improving Large Language Models via Progressive Experience Evolution
Shijie Ren, Xiting Wang, Meng Li +8
Large language models (LLMs) capable of self-improvement require not only effective policy optimization, but also a principled mechanism for transforming transient interaction expe…
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…
SlangDIT: Benchmarking LLMs in Interpretative Slang Translation
Yunlong Liang, Fandong Meng, Jiaan Wang +1
The challenge of slang translation lies in capturing context-dependent semantic extensions, as slang terms often convey meanings beyond their literal interpretation. While slang de…
THOR-MoE: Hierarchical Task-Guided and Context-Responsive Routing for Neural Machine Translation
Yunlong Liang, Fandong Meng, Jie Zhou
The sparse Mixture-of-Experts (MoE) has achieved significant progress for neural machine translation (NMT). However, there exist two limitations in current MoE solutions which may…
An Empirical Study of Many-to-Many Summarization with Large Language Models
Jiaan Wang, Fandong Meng, Zengkui Sun +5
Many-to-many summarization (M2MS) aims to process documents in any language and generate the corresponding summaries also in any language. Recently, large language models (LLMs) ha…
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…
DRT: Deep Reasoning Translation via Long Chain-of-Thought
Jiaan Wang, Fandong Meng, Yunlong Liang +1
Recently, O1-like models have emerged as representative examples, illustrating the effectiveness of long chain-of-thought (CoT) in reasoning tasks such as math and coding tasks. In…
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…
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…
Cross-Lingual Knowledge Editing in Large Language Models
Jiaan Wang, Yunlong Liang, Zengkui Sun +3
Knowledge editing aims to change language models' performance on several special cases (i.e., editing scope) by infusing the corresponding expected knowledge into them. With the re…
Snowman: A Million-scale Chinese Commonsense Knowledge Graph Distilled from Foundation Model
Jiaan Wang, Jianfeng Qu, Yunlong Liang +4
Constructing commonsense knowledge graphs (CKGs) has attracted wide research attention due to its significant importance in cognitive intelligence. Nevertheless, existing CKGs are…
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…
Towards Unifying Multi-Lingual and Cross-Lingual Summarization
Jiaan Wang, Fandong Meng, Duo Zheng +4
To adapt text summarization to the multilingual world, previous work proposes multi-lingual summarization (MLS) and cross-lingual summarization (CLS). However, these two tasks have…
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…
Zero-Shot Cross-Lingual Summarization via Large Language Models
Jiaan Wang, Yunlong Liang, Fandong Meng +4
Given a document in a source language, cross-lingual summarization (CLS) aims to generate a summary in a different target language. Recently, the emergence of Large Language Models…
A Multi-task Multi-stage Transitional Training Framework for Neural Chat Translation
Chulun Zhou, Yunlong Liang, Fandong Meng +5
Neural chat translation (NCT) aims to translate a cross-lingual chat between speakers of different languages. Existing context-aware NMT models cannot achieve satisfactory performa…
Summary-Oriented Vision Modeling for Multimodal Abstractive Summarization
Yunlong Liang, Fandong Meng, Jinan Xu +3
Multimodal abstractive summarization (MAS) aims to produce a concise summary given the multimodal data (text and vision). Existing studies mainly focus on how to effectively use th…