6 citations · 24 across the 55 of their papers we have counts for
24 papers · 1 filter
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…
Retrieval-Augmented Machine Translation with Unstructured Knowledge
Jiaan Wang, Fandong Meng, Yingxue Zhang +1
Retrieval-augmented generation (RAG) introduces additional information to enhance large language models (LLMs). In machine translation (MT), previous work typically retrieves in-co…
Extralonger: Toward a Unified Perspective of Spatial-Temporal Factors for Extra-Long-Term Traffic Forecasting
Zhiwei Zhang, Shaojun E, Fandong Meng +2
Traffic forecasting plays a key role in Intelligent Transportation Systems, and significant strides have been made in this field. However, most existing methods can only predict up…
CRAT: A Multi-Agent Framework for Causality-Enhanced Reflective and Retrieval-Augmented Translation with Large Language Models
Meiqi Chen, Fandong Meng, Yingxue Zhang +2
Large language models (LLMs) have shown great promise in machine translation, but they still struggle with contextually dependent terms, such as new or domain-specific words. This…
On the token distance modeling ability of higher RoPE attention dimension
Xiangyu Hong, Che Jiang, Biqing Qi +4
Length extrapolation algorithms based on Rotary position embedding (RoPE) have shown promising results in extending the context length of language models. However, understanding ho…
DelTA: An Online Document-Level Translation Agent Based on Multi-Level Memory
Yutong Wang, Jiali Zeng, Xuebo Liu +4
Large language models (LLMs) have achieved reasonable quality improvements in machine translation (MT). However, most current research on MT-LLMs still faces significant challenges…
AVG-LLaVA: An Efficient Large Multimodal Model with Adaptive Visual Granularity
Zhibin Lan, Liqiang Niu, Fandong Meng +3
Recently, large multimodal models (LMMs) have achieved significant advancements. When dealing with high-resolution images, dominant LMMs typically divide them into multiple local i…
MaskMamba: A Hybrid Mamba-Transformer Model for Masked Image Generation
Wenchao Chen, Liqiang Niu, Ziyao Lu +2
Image generation models have encountered challenges related to scalability and quadratic complexity, primarily due to the reliance on Transformer-based backbones. In this study, we…
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…
Translatotron-V(ison): An End-to-End Model for In-Image Machine Translation
Zhibin Lan, Liqiang Niu, Fandong Meng +3
In-image machine translation (IIMT) aims to translate an image containing texts in source language into an image containing translations in target language. In this regard, convent…
Beyond Next Token Prediction: Patch-Level Training for Large Language Models
Chenze Shao, Fandong Meng, Jie Zhou
The prohibitive training costs of Large Language Models (LLMs) have emerged as a significant bottleneck in the development of next-generation LLMs. In this paper, we show that it i…
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…
TasTe: Teaching Large Language Models to Translate through Self-Reflection
Yutong Wang, Jiali Zeng, Xuebo Liu +3
Large language models (LLMs) have exhibited remarkable performance in various natural language processing tasks. Techniques like instruction tuning have effectively enhanced the pr…
Touch100k: A Large-Scale Touch-Language-Vision Dataset for Touch-Centric Multimodal Representation
Ning Cheng, Changhao Guan, Jing Gao +7
Touch holds a pivotal position in enhancing the perceptual and interactive capabilities of both humans and robots. Despite its significance, current tactile research mainly focuses…
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…
C-LLM: Learn to Check Chinese Spelling Errors Character by Character
Kunting Li, Yong Hu, Liang He +2
Chinese Spell Checking (CSC) aims to detect and correct spelling errors in sentences. Despite Large Language Models (LLMs) exhibit robust capabilities and are widely applied in var…
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…
Understanding and Addressing the Under-Translation Problem from the Perspective of Decoding Objective
Chenze Shao, Fandong Meng, Jiali Zeng +1
Neural Machine Translation (NMT) has made remarkable progress over the past years. However, under-translation and over-translation remain two challenging problems in state-of-the-a…
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…
On Large Language Models' Hallucination with Regard to Known Facts
Che Jiang, Biqing Qi, Xiangyu Hong +6
Large language models are successful in answering factoid questions but are also prone to hallucination. We investigate the phenomenon of LLMs possessing correct answer knowledge y…
Unsupervised Information Refinement Training of Large Language Models for Retrieval-Augmented Generation
Shicheng Xu, Liang Pang, Mo Yu +4
Retrieval-augmented generation (RAG) enhances large language models (LLMs) by incorporating additional information from retrieval. However, studies have shown that LLMs still face…
Generative Multi-Modal Knowledge Retrieval with Large Language Models
Xinwei Long, Jiali Zeng, Fandong Meng +4
Knowledge retrieval with multi-modal queries plays a crucial role in supporting knowledge-intensive multi-modal applications. However, existing methods face challenges in terms of…