67 citations · 71 across the 17 of their papers we have counts for
18 papers
Adaptive Mutual Distillation for Balanced Multi-Task Post-Training of Large Language Models
Baohang Li, Xiaocheng Feng, Yichong Huang +6
Multi-task post-training of large language models (LLMs) aims to improve performance across tasks with unequal amounts of training data. Existing methods focus primarily on balanci…
ACTD: Anchor-Based Cross-Tokenizer Distillation with Residual Regularization
Huiyi Zhang, Zijian Li, Xiaocheng Feng +4
Knowledge distillation effectively transfers reasoning capabilities from large language models to lightweight student models. To enable knowledge transfer across disparate model fa…
x1: Learning to Think Adaptively Across Languages and Cultures
Yangfan Ye, Xiaocheng Feng, Xiachong Feng +8
Languages encode distinct abstractions and inductive priors, yet most large language models (LLMs) overlook this diversity by reasoning in a single dominant language. In this work,…
Scalable Multilingual Multimodal Machine Translation with Speech-Text Fusion
Yexing Du, Youcheng Pan, Zekun Wang +7
Multimodal Large Language Models (MLLMs) have achieved notable success in enhancing translation performance by integrating multimodal information. However, existing research primar…
PERSONA: Dynamic and Compositional Inference-Time Personality Control via Activation Vector Algebra
Xiachong Feng, Liang Zhao, Weihong Zhong +5
Current methods for personality control in Large Language Models rely on static prompting or expensive fine-tuning, failing to capture the dynamic and compositional nature of human…
CLAIM: Mitigating Multilingual Object Hallucination in Large Vision-Language Models with Cross-Lingual Attention Intervention
Zekai Ye, Qiming Li, Xiaocheng Feng +10
Large Vision-Language Models (LVLMs) have demonstrated impressive multimodal abilities but remain prone to multilingual object hallucination, with a higher likelihood of generating…
FroM: Frobenius Norm-Based Data-Free Adaptive Model Merging
Zijian Li, Xiaocheng Feng, Huixin Liu +3
With the development of large language models, fine-tuning has emerged as an effective method to enhance performance in specific scenarios by injecting domain-specific knowledge. I…
CC-Tuning: A Cross-Lingual Connection Mechanism for Improving Joint Multilingual Supervised Fine-Tuning
Yangfan Ye, Xiaocheng Feng, Zekun Yuan +11
Current large language models (LLMs) often exhibit imbalanced multilingual capabilities due to their English-centric training corpora. To address this, existing fine-tuning approac…
One for All: Update Parameterized Knowledge Across Multiple Models
Weitao Ma, Xiyuan Du, Xiaocheng Feng +8
Large language models (LLMs) encode vast world knowledge but struggle to stay up-to-date, often leading to errors and hallucinations. Knowledge editing offers an efficient alternat…
Enhancing Non-English Capabilities of English-Centric Large Language Models through Deep Supervision Fine-Tuning
Wenshuai Huo, Xiaocheng Feng, Yichong Huang +9
Large language models (LLMs) have demonstrated significant progress in multilingual language understanding and generation. However, due to the imbalance in training data, their cap…
Ensuring Consistency for In-Image Translation
Chengpeng Fu, Xiaocheng Feng, Yichong Huang +9
The in-image machine translation task involves translating text embedded within images, with the translated results presented in image format. While this task has numerous applicat…
Exploring Cross-lingual Latent Transplantation: Mutual Opportunities and Open Challenges
Yangfan Ye, Xiaocheng Feng, Xiachong Feng +11
Current large language models (LLMs) often exhibit imbalances in multilingual capabilities and cultural adaptability, largely attributed to their English-centric pre-training data.…
Ensemble Learning for Heterogeneous Large Language Models with Deep Parallel Collaboration
Yichong Huang, Xiaocheng Feng, Baohang Li +4
Large language models (LLMs) exhibit complementary strengths in various tasks, motivating the research of LLM ensembling. However, existing work focuses on training an extra reward…
Relay Decoding: Concatenating Large Language Models for Machine Translation
Chengpeng Fu, Xiaocheng Feng, Yichong Huang +5
Leveraging large language models for machine translation has demonstrated promising results. However, it does require the large language models to possess the capability of handlin…
Aligning Translation-Specific Understanding to General Understanding in Large Language Models
Yichong Huang, Baohang Li, Xiaocheng Feng +4
Large Language models (LLMs) have exhibited remarkable abilities in understanding complex texts, offering a promising path towards human-like translation performance. However, this…
Towards Higher Pareto Frontier in Multilingual Machine Translation
Yichong Huang, Xiaocheng Feng, Xinwei Geng +2
Multilingual neural machine translation has witnessed remarkable progress in recent years. However, the long-tailed distribution of multilingual corpora poses a challenge of Pareto…
Unifying the Convergences in Multilingual Neural Machine Translation
Yichong Huang, Xiaocheng Feng, Xinwei Geng +1
Although all-in-one-model multilingual neural machine translation (multilingual NMT) has achieved remarkable progress, the convergence inconsistency in the joint training is ignore…
The Factual Inconsistency Problem in Abstractive Text Summarization: A Survey
Yichong Huang, Xiachong Feng, Xiaocheng Feng +1
Recently, various neural encoder-decoder models pioneered by Seq2Seq framework have been proposed to achieve the goal of generating more abstractive summaries by learning to map in…