Papers by Shujian Liu
LanguageFlow: Advancing Diffusion Language Generation with Probabilistic Flows (2024.naacl-long)
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| Challenge: | Recent work has demonstrated success in controlling sentence attributes and structure based on diffusion language models. |
| Approach: | They propose a language-rectified flow method that reformulates standard probabilistic flow models to learn ordinary differential equations to transport between the source and target distributions. |
| Outcome: | The proposed method outperforms baselines on three fine-grained control tasks and multiple high-quality text editing tasks. |
IMTLab: An Open-Source Platform for Building, Evaluating, and Diagnosing Interactive Machine Translation Systems (2023.emnlp-main)
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Xu Huang, Zhirui Zhang, Ruize Gao, Yichao Du, Lemao Liu, Guoping Huang, Shuming Shi, Jiajun Chen, Shujian Huang
| Challenge: | Existing systems that use a left-to-right completion paradigm are inefficient and expensive. |
| Approach: | They propose an open-source end-to-end interactive machine translation system platform . they propose to use a prefix-constrained decoding approach to achieve end- to-end evaluation . |
| Outcome: | The proposed system can guarantee high-quality, error-free translations . it uses prefix-constrained decoding and improves on previous systems . |
MAPO: Advancing Multilingual Reasoning through Multilingual-Alignment-as-Preference Optimization (2024.acl-long)
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| Challenge: | Existing models exhibit inconsistent reasoning abilities across different languages . existing models lack consistency across languages due to imbalance of training data . |
| Approach: | They propose a multilingual alignment-as-preference optimization framework to align reasoning processes in other languages with the dominant language. |
| Outcome: | The proposed framework improves multilingual reasoning across languages on three benchmarks. |
BiTIIMT: A Bilingual Text-infilling Method for Interactive Machine Translation (2022.acl-long)
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| Challenge: | Existing IMT systems relying on lexical constrained decoding (LCD) are limited in translation efficiency and quality due to LCD. |
| Approach: | They propose a novel interactive neural machine translation system that uses lexical constraints to decode missing words in a manually revised translation. |
| Outcome: | The proposed system performs significantly better and faster than state-of-the-art IMT on three translation tasks. |
Multilingual Contrastive Decoding via Language-Agnostic Layers Skipping (2024.findings-emnlp)
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| Challenge: | Decoding by contrasting layers (DoLa) is designed to improve the generation quality of large language models (LLMs) however, this approach does not work well on non-English tasks. |
| Approach: | They propose a contrastive decoding algorithm that uses amateur logits to contrast with the output of an expert model's early exit logits. |
| Outcome: | The proposed method outperforms baselines and significantly improves chain-of-thought reasoning accuracy across 11 languages. |
kNN-BOX: A Unified Framework for Nearest Neighbor Generation (2024.eacl-demo)
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| Challenge: | kNN-BOX enables quick development and visualization for novel generation paradigm . Currently, knn-BOx has provided implementation of seven popular kN-MT variants . |
| Approach: | They propose a framework which decomposes the datastore-augmentation approach into three modules . they apply kNN-BOX to machine translation and three other tasks . |
| Outcome: | The proposed framework decomposes the datastore-augmentation approach into three modules . it provides implementation of seven popular kNN-MT variants, covering research from performance enhancement to efficiency optimization. |
Large Language Models Are Cross-Lingual Knowledge-Free Reasoners (2025.naacl-long)
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| Challenge: | Large language models have demonstrated impressive reasoning capabilities across multiple languages, but the relationship between capabilities in different languages is less explored. |
| Approach: | They decompose the process of reasoning tasks into two separate components: knowledge retrieval and knowledge-free reasoning. |
| Outcome: | The proposed model can be transferred across source-target languages despite secondary impact of resource in some specific target languages, while cross-lingual knowledge retrieval significantly hinders the transfer. |
EfficientRAG: Efficient Retriever for Multi-Hop Question Answering (2024.emnlp-main)
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Ziyuan Zhuang, Zhiyang Zhang, Sitao Cheng, Fangkai Yang, Jia Liu, Shujian Huang, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang, Qi Zhang
| Challenge: | Existing retrieval-augmented generation methods rely on multiple calls of large language models (LLMs) Large-language models lack knowledge underrepresented in training data and still face hallucinations. |
| Approach: | They propose an efficient retriever for multi-hop question answering that generates new queries iteratively without the need for LLM calls. |
| Outcome: | The proposed method surpasses existing methods on three open-domain multi-hop question-answering datasets. |
ALLSH: Active Learning Guided by Local Sensitivity and Hardness (2022.findings-naacl)
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| Challenge: | Existing studies show that labeling in crowdsourcing annotations is not an annotation artifact but rather a core linguistic phenomenon. |
| Approach: | They propose to retrieve unlabeled data with a local sensitivity and hardness-aware acquisition function. |
| Outcome: | The proposed method achieves consistent gains over the commonly used active learning strategies in various classification tasks. |
R-PRM: Reasoning-Driven Process Reward Modeling (2025.emnlp-main)
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| Challenge: | Existing Process Reward Models (PRMs) output evaluation scores directly, limiting both learning efficiency and evaluation accuracy. |
| Approach: | They propose a Reasoning-Driven Process Reward Modeling (R-PRM) which activates inherent reasoning to enhance process-level evaluation. |
| Outcome: | The proposed model outperforms baseline models on ProcessBench and PRMBench by 13.9 and 8.5 F1 scores. |
Multilingual Machine Translation with Large Language Models: Empirical Results and Analysis (2024.findings-naacl)
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Wenhao Zhu, Hongyi Liu, Qingxiu Dong, Jingjing Xu, Shujian Huang, Lingpeng Kong, Jiajun Chen, Lei Li
| Challenge: | Existing studies show that large language models (LLMs) can handle multilingual machine translation (MMT) However, the multilingual translation ability of LLMs remains under-explored. |
| Approach: | They evaluate eight popular LLMs including ChatGPT and GPT-4 to determine their performance in multilingual machine translation. |
| Outcome: | The proposed model can generate moderate translation even on zero-resource languages and cross-lingual exemplars can provide better task guidance for low-resourced translation than exemplar in the same language pairs. |
Passage-Mask: A Learnable Regularization Strategy for Retriever-Reader Models (2022.emnlp-main)
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| Challenge: | Xu et al., 2021) find that retrieval-reader models overfit top-rank passages and fail to reason over entire retrieval passages. |
| Approach: | They propose a passage mask mechanism which desensitizes the impact from top-rank retrieval passages and prevents the model from overfitting. |
| Outcome: | Experiments on open question answering, dialogue conversation, and fact verification show that the proposed model outperforms baselines. |
Process-based Self-Rewarding Language Models (2025.findings-acl)
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| Challenge: | Existing methods to reward LLMs' outputs are not effective in mathematical reasoning scenarios and may lead to a decline in performance. |
| Approach: | They propose a process-based self-rewarding pipeline that integrates long-thought reasoning, step-wise LLM-as-a-Judge, and step- wise preference optimization within the existing paradigm. |
| Outcome: | The proposed model improves the performance of Large Language Models on multiple mathematical reasoning benchmarks and shows that it can surpass human capabilities. |
ContextCheck: Sentence-Level Faithfulness Verification with Context-Aware Disambiguation (2026.findings-acl)
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Yueqin Yin, Yaxi Li, Xin Liu, Xun Wang, Kaiqiang Song, Simin Ma, Shujian Liu, Sathish Reddy Indurthi, Haoyun Deng, Pengcheng He, Mingyuan Zhou, Song Wang
| Challenge: | Large language models often hallucinate, producing content that is factually incorrect or not grounded in the sources. |
| Approach: | They propose a framework for sentence-level faithfulness verification with context-aware disambiguation. |
| Outcome: | The proposed framework improves Macro F1 by over 10 points compared to baselines on three context-dependent datasets. |