Papers by Junnan Liu
LATENTLOGIC: Learning Logic Rules in Latent Space over Knowledge Graphs (2023.findings-emnlp)
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| Challenge: | Existing methods for learning logic rules for knowledge graph reasoning face limitations such as searching in vast search space and inefficient optimization. |
| Approach: | They propose a framework to efficiently mine logic rules by controllable generation in the latent space by a pre-trained VAE and a discriminator. |
| Outcome: | The proposed framework efficiently mines logic rules by controllable generation in the latent space. |
SARA: Salience-Aware Reinforced Adaptive Decoding for Large Language Models in Abstractive Summarization (2025.acl-long)
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| Challenge: | Existing decoding strategies neglect the explicit use of salient contextual information and rely on static hyperparameters to fix the balance between contextual and prior knowledge. |
| Approach: | They propose a salience-aware reinforced adaptive decoding (SARA) which incorporates salient contextual information and allows the model to determine reliance on source document's context, salient context, and model's prior knowledge based on pointwise mutual information. |
| Outcome: | The proposed model improves the quality and faithfulness of summaries across LLMs without modifying their weights. |
CompassVerifier: A Unified and Robust Verifier for LLMs Evaluation and Outcome Reward (2025.emnlp-main)
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Shudong Liu, Hongwei Liu, Junnan Liu, Linchen Xiao, Songyang Gao, Chengqi Lyu, Yuzhe Gu, Wenwei Zhang, Derek F. Wong, Songyang Zhang, Kai Chen
| Challenge: | Existing approaches lack robustness to handle complex edge cases and generalizability across different domains. |
| Approach: | They develop an accurate and lightweight verifier model for evaluation and outcome reward that matches unstructured outputs against standard answers. |
| Outcome: | The proposed model can process multiple answer types including multi-subproblems, formulas, and sequence answers while identifying abnormal/invalid responses. |
ProBench: Judging Multimodal Foundation Models on Open-ended Multi-domain Expert Tasks (2025.findings-acl)
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| Challenge: | Solving expert-level multimodal tasks requires strong user query understanding, domain-specific knowledge, and advanced reasoning abilities. |
| Approach: | They propose a benchmark of open-ended user queries encapsulating professional expertise and advanced reasoning. |
| Outcome: | The proposed benchmark is publicly accessible at TBC. |
Noise-injected Consistency Training and Entropy-constrained Pseudo Labeling for Semi-supervised Extractive Summarization (2022.coling-1)
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| Challenge: | Existing studies on semi-supervised learning methods focus on how to effectively utilize abundant unlabeled data. |
| Approach: | They propose a semi-supervised consistency training method to regularize model predictions and a pseudo-labeling strategy to obtain high-confidence labels from unlabeled predictions. |
| Outcome: | The proposed method improves extractive summarization over an insufficient labeled dataset. |
Are Your LLMs Capable of Stable Reasoning? (2025.findings-acl)
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Junnan Liu, Hongwei Liu, Linchen Xiao, Ziyi Wang, Kuikun Liu, Songyang Gao, Wenwei Zhang, Songyang Zhang, Kai Chen
| Challenge: | Existing evaluation protocols and metrics do not capture the full spectrum of LLM capabilities, especially in complex reasoning tasks. |
| Approach: | They propose a new evaluation metric that continuously assesses model performance across multiple sampling attempts, quantifying both the model’s potential capabilities and operational consistency. |
| Outcome: | The proposed evaluation metric measures model performance across multiple sampling attempts and provides comprehensive insights into their potential capabilities and operational consistency. |
Enhancing Multilingual Reasoning via Steerable Model Merging (2026.findings-acl)
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Zhuoran Li, Rui Xu, Jian Yang, Junnan Liu, Zhijun Chen, Qianren Mao, Hongcheng Guo, Jiaheng Liu, Likang Xiao, Ming LI, Xiaojie Wang
| Challenge: | Model merging is an effective technique for composing the capabilities of a multilingual model and a reasoning model. |
| Approach: | They propose a model merging framework that modulates the contribution of each source model. |
| Outcome: | Experiments show that the proposed model merging framework outperforms strong baselines on multilingual reasoning benchmarks across 21 different languages. |
Few-Shot Natural Language to First-Order Logic Translation via Code Generation (2025.naacl-long)
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| Challenge: | Recent studies have focused on translation of natural language to first-order logical formula (NL-FOL) but these methods face challenges such as inconsistency between training and inference phases and data-intensive finetuning process. |
| Approach: | They propose a method for translating natural language into first-order logical formulas using code snippets. |
| Outcome: | The proposed method surpasses training-free baselines and is comparable to supervised models trained on the full training data. |
FocalOrder: Focal Preference Optimization for Reading Order Detection (2026.acl-long)
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Fuyuan Liu, Dianyu Yu, He Ren, Nayu Liu, Xiaomian Kang, Delai Qiu, Fa Zhang, Genpeng Zhen, Shengping Liu, Liang Jiaen, null Weihuang, Yining Wang, Junnan Zhu
| Challenge: | Existing methods for document comprehension rely on uniform supervision, resulting in a performance degradation in the intermediate sections. |
| Approach: | They propose a framework driven by Focal Preference Optimization to detect reading order in document layouts. |
| Outcome: | The proposed framework outperforms competing baselines and surpasses large-scale general VLMs. |
Lightweight Contenders: Navigating Semi-Supervised Text Mining through Peer Collaboration and Self Transcendence (2025.findings-naacl)
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| Challenge: | Existing frameworks for semi-supervised text mining with lightweight models are limited by label data scarcity. |
| Approach: | They propose a framework for semi-supervised text mining with lightweight models . it incorporates online distillation to train lightweight student models by imitating the Teacher model . |
| Outcome: | The proposed framework exhibits notable performance enhancements over existing frameworks. |
GenProve: Learning to Generate Text with Fine-Grained Provenance (2026.acl-long)
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| Challenge: | Existing methods for large language models (LLMs) are coarse-grained and fail to distinguish between direct quotes and complex reasoning. |
| Approach: | They propose a framework that combines supervised fine-tuning and group relative policy optimization to generate fluent answers while simultaneously producing sentence-level provenance triples. |
| Outcome: | The proposed framework outperforms 14 strong large language models in joint evaluation. |
MSMO: Multimodal Summarization with Multimodal Output (D18-1)
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| Challenge: | Existing studies show that multimodal summarization can improve user satisfaction for informativeness of summaries by using information in visual modality. |
| Approach: | They propose a task to generate text and select the most relevant image from the multimodal input and a novel multimodal automatic evaluation method to evaluate multimodal outputs. |
| Outcome: | The proposed method improves user satisfaction by 12.4% compared to the current system . |
QUEST: Efficient Extreme Multi-Label Text Classification with Large Language Models on Commodity Hardware (2024.findings-emnlp)
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| Challenge: | Extreme multi-label text classification (EMTC) involves predicting multiple labels from a vast pool of candidates based on a user’s textual query. |
| Approach: | They propose a Quantized and Efficient Learning with Sampling Technique that uses a hash sampling module to reduce the data volume to one-fourth of its original size. |
| Outcome: | Extensive experiments show that QUEST outperforms existing methods while requiring fewer computational resources. |