Papers by Chao Tong
Shared-Private Bilingual Word Embeddings for Neural Machine Translation (P19-1)
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| Challenge: | Word embedding is central to neural machine translation, but indirectly interfaces with other layers, making them comparatively isolated. |
| Approach: | They propose a shared-private bilingual word embedding which gives a closer relationship between the source and target embedders and reduces the number of model parameters. |
| Outcome: | The proposed model improves on 5 language pairs belonging to 6 different language families and written in 5 different alphabets and significantly reduces model parameters. |
Think Wider, Detect Sharper: Reinforced Reference Coverage for Document-Level Self-Contradiction Detection (2025.emnlp-main)
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| Challenge: | Recent approaches to document-level contradiction detection (DSCD) only gain marginal improvement and often introduce inconsistencies across repeated responses. |
| Approach: | They propose a method that combines supervised fine-tuning and reinforcement learning to enhance document-level contradiction detection (DSCD) they propose to use a task-specific reward function to expand the model’s reasoning scope, boosting both accuracy and consistency. |
| Outcome: | The proposed method significantly boosts Llama 3.1-8B-Instruct’s accuracy from 38.5% to 51.1%, and consistency from 59.6% to76.2%. |
Modeling Localness for Self-Attention Networks (D18-1)
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| Challenge: | Existing approaches to model locality for self-attention networks have shown great value for capturing global dependencies. |
| Approach: | They propose to model localness for self-attention networks to capture local context . they cast localness modeling as a learnable Gaussian bias, which indicates the central and scope of the local region to be paid more attention. |
| Outcome: | The proposed model improves the ability to capture local context and improves accuracy. |
Streamlining the Collaborative Chain of Models into A Single Forward Pass in Generation-Based Tasks (2025.findings-acl)
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| Challenge: | Recent advances in "Chain of Models" approach increase resource demands as each model must be deployed separately. |
| Approach: | They propose a prompt-tuning method that enables models to share hidden states . they modify input and attention masks during training to eliminate redundant forward passes . |
| Outcome: | Empirical results show that FTHSS matches the performance of traditional model chains while improving inference efficiency. |
ImpRIF: Stronger Implicit Reasoning Leads to Better Complex Instruction Following (2026.acl-long)
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| Challenge: | Experiments show that enhancing implicit reasoning capabilities can significantly improve complex instruction following in large language models. |
| Approach: | They propose a method to enhance LLMs’ understanding of implicit reasoning instructions by formalizing such instructions as verifiable reasoning graphs and fine-tuning with graph reasoning. |
| Outcome: | The proposed method outperforms existing models on five complex instruction following benchmarks and will be open-sourced in the near future. |
Retrieve-Plan-Generation: An Iterative Planning and Answering Framework for Knowledge-Intensive LLM Generation (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) often produce factual errors due to limited internal knowledge. |
| Approach: | They propose a retrieval-augmented generation framework that generates plan tokens to guide subsequent generation. |
| Outcome: | The proposed framework improves the accuracy of large language models with external knowledge sources. |
Learning Deep Transformer Models for Machine Translation (P19-1)
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| Challenge: | Neural machine translation models have advanced the previous state-of-the-art by learning mappings between sequences via neural networks and attention mechanisms. |
| Approach: | They propose to use layer normalization to pass the combination of previous layers to the next layer to improve the model. |
| Outcome: | The proposed model outperforms the shallow Transformer-Big/Base baseline model on English-German and Chinese-English tasks by 0.4-2.4 BLEU points. |