Papers by Jianhao Zhang
RefuteBench: Evaluating Refuting Instruction-Following for Large Language Models (2024.findings-acl)
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| Challenge: | The application scope of large language models (LLMs) is expanding . however, evaluating whether models can respond to user feedback has not been thoroughly analyzed. |
| Approach: | They propose a benchmark to assess whether large language models can respond to refuting feedback and adhere to user demands throughout the conversation. |
| Outcome: | The proposed benchmark covers tasks such as question answering, machine translation, and email writing. |
Non-Autoregressive Document-Level Machine Translation (2023.findings-emnlp)
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| Challenge: | Existing non-autoregressive translation models struggle with document context and handling discourse phenomena. |
| Approach: | They propose a simple but effective design of sentence alignment between source and target to improve their performance on document-level machine translation. |
| Outcome: | The proposed model achieves high acceleration on documents and sentence alignment significantly enhances their performance. |
CAKE: Causal-Guided Adaptive Knowledge Editing for LLMs (2026.acl-long)
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| Challenge: | Existing knowledge editing methods rely on fixed-layer selection and uniform residual assignment, ignoring heterogeneous causal efficacy of different layers. |
| Approach: | They propose a method that allows for a causally-guided adaptive knowledge editing that combines causal tracing scores with a constrained quadratic optimization problem. |
| Outcome: | The proposed method achieves comparable performance with comparable overhead. |
PALT: Parameter-Lite Transfer of Language Models for Knowledge Graph Completion (2022.findings-emnlp)
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| Challenge: | Pretrained language models (LMs) are a powerful transfer learning approach for knowledge graph (KG) completion. |
| Approach: | They propose a parameter-lite transfer learning approach for pretrained language models for knowledge graph (KG) completion. |
| Outcome: | The proposed model outperforms the state-of-the-art models on a knowledge graph completion benchmark by tuning 1% of the parameters. |
TRIGO: Benchmarking Formal Mathematical Proof Reduction for Generative Language Models (2023.emnlp-main)
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Jing Xiong, Jianhao Shen, Ye Yuan, Haiming Wang, Yichun Yin, Zhengying Liu, Lin Li, Zhijiang Guo, Qingxing Cao, Yinya Huang, Chuanyang Zheng, Xiaodan Liang, Ming Zhang, Qun Liu
| Challenge: | Automated theorem proving (ATP) benchmarks focus on symbolic inference but rarely involve understanding complex number combination reasoning. |
| Approach: | They propose a benchmark that requires a model to reduce a trigonometric expression with step-by-step proof and evaluates a generative LM’s reasoning ability on formulas and ability to manipulate, group, and factor number terms. |
| Outcome: | The proposed benchmark evaluates a generative LM’s reasoning ability on formulas and ability to manipulate, group, and factor number terms. |
Keys to Robust Edits: From Theoretical Insights to Practical Advances (2025.acl-long)
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| Challenge: | Existing methods for modifying parametric memory are prone to inaccuracies due to conflicting or outdated information. |
| Approach: | They propose a plug-and-play module that disentangles editing keys from native model representations and dynamically adjusts keys via contrastive learning to achieve robustness-specificity balance. |
| Outcome: | The proposed method improves over robustness tests by up to 66.4% while maintaining the success rate unaffected. |
DC-MBR: Distributional Cooling for Minimum Bayesian Risk Decoding (2024.lrec-main)
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| Challenge: | Existing methods for decoding target language are degenerate, hallucinating or empty. |
| Approach: | They propose a method that tunes down the Softmax temperature to reduce autoregressive over-smoothness by label smoothing the output distributions. |
| Outcome: | The proposed method improves MBR in various settings. |
Generate & Rank: A Multi-task Framework for Math Word Problems (2021.findings-emnlp)
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| Challenge: | Existing studies formalize MWP as a generation task but mathematical expressions are prone to minor mistakes. |
| Approach: | They propose a ranking task for math word problem (MWP) that learns from its own mistakes and distinguishes between correct and incorrect expressions. |
| Outcome: | The proposed model outperforms baselines on the classical Math23k dataset and is 7% higher than the state-of-the-art. |
BAPO: Boundary-Aware Policy Optimization for Reliable Agentic Search (2026.findings-acl)
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Shiyu Liu, Yongjing Yin, Jianhao Yan, Yunbo Tang, Qinggang Zhang, Bei Li, Xin Chen, Jingang Wang, Xunliang Cai, Jinsong Su
| Challenge: | Existing RL-based agentic search models fail to recognize reasoning boundaries and rarely admit "I DON'T KNOW" lack of reliability leads to plausible but unreliable answers, introducing significant risks . |
| Approach: | They propose a framework to cultivate reliable boundary awareness without compromising accuracy. |
| Outcome: | Experiments show that the proposed framework improves the reliability of agentic search models. |
AlphaEdit+: Model Editing in the Presence of Conflicting and Inconsistent Knowledge (2026.findings-acl)
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| Challenge: | Existing methods for knowledge editing struggle with knowledge conflicts and inconsistencies. |
| Approach: | They propose a new method for knowledge editing that relaxes null-space constraints and introduces a weighting scheme to mitigate conflicts between new and historical knowledge. |
| Outcome: | The proposed method outperforms existing methods on challenging datasets and outperformed existing methods. |
Explicit Syntactic Guidance for Neural Text Generation (2023.acl-long)
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| Challenge: | Existing text generation models follow the sequence-to-sequence paradigm . generative grammar suggests humans generate language by learning language grammar . |
| Approach: | They propose a syntax-guided generation schema that searches the syntax tree in a top-down direction. |
| Outcome: | The proposed method outperforms autoregressive baselines on paraphrase generation and machine translation. |
What Have We Achieved on Non-autoregressive Translation? (2024.findings-acl)
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| Challenge: | Existing studies have shown that non-autoregressive (NAT) methods underperform autoregressive methods (AT) however, their evaluation using BLEU has been shown to weakly correlate with human annotations. |
| Approach: | They propose to evaluate four representative NAT methods using BLEU to narrow the performance gap between autoregressive and autoregressive translations. |
| Outcome: | The proposed methods underperform NAT and autoregressive methods under more reliable evaluation metrics. |
Empirical Prior for Text Autoencoders (2024.findings-emnlp)
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| Challenge: | Variational Autoencoders (VAE) are used to train generative models with latent variables. |
| Approach: | They propose a transition from Variational Autoencoders (VAE) to text autoencodeurs (AE) which model a compact latent space and preserves the capability of the language model itself. |
| Outcome: | The proposed method generates higher quality and more diverse text than the VAE-based Transformer baselines, and is more efficient than previous approaches. |
Dynamics of Instruction Fine-Tuning for Chinese Large Language Models (2025.coling-main)
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| Challenge: | Instruction tuning is a burgeoning method to elicit the general intelligence of Large Language Models. |
| Approach: | They investigate the effects of data quantity, model size, and data construction methods on instruction tuning for Chinese LLMs. |
| Outcome: | The proposed model includes over 40,000 high-quality instruction instances covering ten underlying abilities. |
Unveiling Attractor Cycles in Large Language Models: A Dynamical Systems View of Successive Paraphrasing (2025.acl-long)
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| Challenge: | Dynamical systems theory provides a framework for understanding iterative processes and evolution over time. |
| Approach: | They propose to apply this perspective to large language models which iteratively map input text to output text and re-express meaning with linguistic variation. |
| Outcome: | The proposed model reveals that paraphrases re-express meaning with linguistic variation limiting linguistic diversity . |
Discover and Prove: An Open-source Agentic Framework for Hard Mode Automated Theorem Proving in Lean 4 (2026.acl-long)
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Chengwu Liu, Yichun Yin, Ye Yuan, Jiaxuan Xie, Botao Li, Siqi Li, Jianhao Shen, Yan Xu, Lifeng Shang, Ming Zhang
| Challenge: | Existing approaches to solving mathematical problems fall into two broad categories: informal methods and formal methods. |
| Approach: | They propose to use LLM natural-language reasoning to discover answers . they introduce Discover And Prove framework that rewrites Hard Mode statements into Easy Mode ones for existing ATP provers. |
| Outcome: | The proposed framework can be used to prove hard mode statements on ATP benchmarks. |
Revisiting Cross-Lingual Summarization: A Corpus-based Study and A New Benchmark with Improved Annotation (2023.acl-long)
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Yulong Chen, Huajian Zhang, Yijie Zhou, Xuefeng Bai, Yueguan Wang, Ming Zhong, Jianhao Yan, Yafu Li, Judy Li, Xianchao Zhu, Yue Zhang
| Challenge: | Existing work on cross-lingual summarization (CLS) does not consider crosslingual sources for summarizing. |
| Approach: | They propose a cross-lingual conversation summarization benchmark that explicitly considers source context. |
| Outcome: | The proposed method surpasses baselines on ConvSumX and 3 widely-used manual annotations. |
Takin-VC: Expressive Zero-Shot Voice Conversion via Adaptive Hybrid Content Encoding and Enhanced Timbre Modeling (2025.acl-long)
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Yang Yuguang, Yu Pan, Jixun Yao, Xiang Zhang, Jianhao Ye, Hongbin Zhou, Lei Xie, Lei Ma, Jianjun Zhao
| Challenge: | Expressive zero-shot voice conversion (VC) aims to modify source timbre to match unseen speaker . existing zero- shot VC systems struggle to reproduce paralinguistic information in highly expressive speech . |
| Approach: | They propose a framework for expressive zero-shot voice conversion that uses hybrid content encoding and memory-augmented context-aware timbre modeling. |
| Outcome: | The proposed framework surpasses state-of-the-art VC systems in speech naturalness, speaker similarity, and speaker similarness. |
Measuring Social Norms of Large Language Models (2024.findings-naacl)
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| Challenge: | Existing datasets that evaluate a general understanding of social science are inadequate to understand social norms. |
| Approach: | They propose a multi-agent framework to improve large language models’ ability to understand social norms by comparing them to elementary students. |
| Outcome: | The proposed framework improves large language models to be on par with humans. |