Papers by Qian Lou
CR-UTP: Certified Robustness against Universal Text Perturbations on Large Language Models (2024.findings-acl)
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| Challenge: | Existing certified robustness methods for certifying input-specific text perturbations have shown promise in certifyling UTPs, but masking only adversarial words can eliminate the attack. |
| Approach: | They propose a method to certify a language model’s robustness against UTPs by using random smoothing. |
| Outcome: | The proposed method achieves high certified accuracy under extensive masking and achieves state-of-the-art results in multiple settings. |
UniLG: A Unified Structure-aware Framework for Lyrics Generation (2023.acl-long)
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| Challenge: | Existing works ignore musical attributes hidden behind lyrics and structure of lyrics . existing works ignore structure of generated lyrics and do not consider structure of songs . |
| Approach: | They propose a framework for conditional lyrics generation that considers structure and relationship between lyrics and music. |
| Outcome: | The proposed framework improves the structure modeling and unifies different conditions for different types of lyrics generation. |
Less is More: Knowledge-Aware Compression for Long Legal Judgment Prediction (2026.findings-acl)
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| Challenge: | Recent advances leverage large language models (LLMs) for legal reasoning, but they face high computational costs and information degradation when handling long cases. |
| Approach: | They propose a framework that selectively retains legally relevant information while reducing redundant or less informative content, enabling efficient and accurate long-context reasoning. |
| Outcome: | The proposed framework outperforms existing methods on four real-world datasets spanning multiple jurisdictions and languages. |
“What Do You Mean by That?” A Parser-Independent Interactive Approach for Enhancing Text-to-SQL (2020.emnlp-main)
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| Challenge: | In Natural Language Interfaces to Databases systems, text-to-SQL parsers allow users to query databases by using natural language questions. |
| Approach: | They propose a parser-independent interactive approach that interacts with users using multi-choice questions and can easily work with arbitrary parsers. |
| Outcome: | The proposed approach improves performance with limited interaction turns by using simulation and human evaluation on two cross-domain datasets with five state-of-the-art parsers. |
Reasoning Like Program Executors (2022.emnlp-main)
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Xinyu Pi, Qian Liu, Bei Chen, Morteza Ziyadi, Zeqi Lin, Qiang Fu, Yan Gao, Jian-Guang Lou, Weizhu Chen
| Challenge: | Existing language models are inadequate in reasoning, according to studies . a new reasoning pre-training paradigm is based on pretraining language models with programs . |
| Approach: | They propose a reasoning pre-training paradigm that empowers language models to harvest reasoning knowledge possessed by program executors. |
| Outcome: | The proposed reasoning pre-training paradigm can boost models' reasoning skills . it can be instantiated by different kinds of program executors and run on a single database . |
LEMON: Language-Based Environment Manipulation via Execution-Guided Pre-training (2022.findings-emnlp)
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| Challenge: | Existing approaches to language-based environment manipulation are difficult to generalize across environments. |
| Approach: | They propose a general framework for language-based environment manipulation tasks that can deal with various environments using the same generative language model. |
| Outcome: | The proposed framework achieves new state-of-the-art results on four of the tasks and the execution-guided pre-training strategy brings remarkable improvements on all experimental tasks. |
Exploring the Secrets Behind the Learning Difficulty of Meaning Representations for Semantic Parsing (2022.emnlp-main)
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| Challenge: | Existing studies show that the design of Meaning Representation (MR) greatly influences the final model performance of a neural semantic parser. |
| Approach: | They propose a data-aware metric called ISS to measure the final performance of MRs. |
| Outcome: | The proposed metric denoting incremental structural stability (ISS) of MRs can be used as an indicator for MR design to avoid the costly training-testing process. |
BadFair: Backdoored Fairness Attacks with Group-conditioned Triggers (2024.findings-emnlp)
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| Challenge: | *BadFair* is a backdoored fairness attack methodology that discriminates and produces incorrect results for specific groups. |
| Approach: | They propose a backdoored fairness attack methodology that discriminates and produces incorrect results for specific groups. |
| Outcome: | The proposed model achieves 85% success rate in attacks aimed at target groups on average while only incurring a minimal accuracy loss. |
ReTraCk: A Flexible and Efficient Framework for Knowledge Base Question Answering (2021.acl-demo)
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| Challenge: | Existing neural semantic parsing methods for knowledge base question answering are lacking . a generic and extensible framework is lacking for KBQA. |
| Approach: | They propose a neural semantic parsing framework for large scale knowledge base question answering . they propose 'retriever-transducer-checker' framework that provides a retriever and a transducer . |
| Outcome: | The proposed framework is ranked at top1 overall performance on the GrailQA leaderboard and achieves competitive performance on typical WebQuestionsSP benchmark. |
Factuality Beyond Coherence: Evaluating LLM Watermarking Methods for Medical Texts (2025.findings-emnlp)
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| Challenge: | Existing benchmarks focus on detection-quality tradeoffs and overlook factual risks. |
| Approach: | They propose a method that assesses factual accuracy and coherence . they use a factor-weighted score to prioritize factual accurate beyond coherency . |
| Outcome: | The proposed method assesses factual accuracy and coherence in medical text . it shows current watermarking methods substantially compromise medical factuality . |
Conjunctive Prompt Attacks in Multi-Agent LLM Systems (2026.acl-long)
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| Challenge: | Existing defenses do not reliably stop the attack because no single component appears malicious in isolation. |
| Approach: | They study conjunctive prompt attacks where trigger key and adversarial template appear benign alone but activate harmful behavior when routing brings them together. |
| Outcome: | The proposed model significantly improves performance over baselines while keeping false activations low. |
Learning Algebraic Recombination for Compositional Generalization (2021.findings-acl)
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Chenyao Liu, Shengnan An, Zeqi Lin, Qian Liu, Bei Chen, Jian-Guang Lou, Lijie Wen, Nanning Zheng, Dongmei Zhang
| Challenge: | Neural sequence models exhibit limited compositional generalization ability in semantic parsing tasks. |
| Approach: | They propose an end-to-end neural model to learn algebraic recombination for compositional generalization. |
| Outcome: | The proposed model is based on two realistic and comprehensive compositional generalization benchmarks. |
Numerical Optimizations for Weighted Low-rank Estimation on Language Models (2022.emnlp-main)
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| Challenge: | Singular value decomposition (SVD) is one of the most popular methods for estimating a target matrix with smaller matrices. |
| Approach: | They propose a method that approximates a target matrix with smaller matrices by two smaller . they also propose metric to predict when the SVD may introduce a significant performance drop. |
| Outcome: | The proposed method can perform better than current SOTA methods in compressing Transformer-based language models. |
You Impress Me: Dialogue Generation via Mutual Persona Perception (2020.acl-main)
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| Challenge: | Existing chit-chat systems tend to generate uninformative responses and lack coherent personality traits due to the diversity of speakers. |
| Approach: | They propose a transmitter-receiver framework which explicitly models understanding between interlocutors. |
| Outcome: | The proposed framework improves on a large public dataset, Persona-Chat, with a significant boost over the state-of-the-art frameworks. |
Chase: A Large-Scale and Pragmatic Chinese Dataset for Cross-Database Context-Dependent Text-to-SQL (2021.acl-long)
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| Challenge: | XDTS is a cross-database context-dependent text-to-sql problem with wide range of applications. |
| Approach: | They present a large-scale Chinese dataset for cross-database context-dependent Text-to-SQL . they find that only 35% of questions are context-independent and 28% of SQL queries are easy . |
| Outcome: | The proposed approach achieves an exact match accuracy of 40% over all questions and 16% over all question sequences. |
Jailbreaking LLMs with Arabic Transliteration and Arabizi (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) are vulnerable to ‘jailbreak’ attacks, focusing on the Arabic language and its various forms. |
| Approach: | They propose to use Arabic transliteration and chatspeak to generate unsafe content on platforms like OpenAI GPT-4 and Anthropic Claude 3 Sonnet. |
| Outcome: | The proposed model could generate unsafe content on platforms like OpenAI GPT-4 and Anthropic Claude 3 Sonnet, highlighting the need for more comprehensive safety training across all language forms. |
A Split-and-Recombine Approach for Follow-up Query Analysis (D19-1)
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| Challenge: | Context-dependent semantic parsing has proved to be an important but challenging task. |
| Approach: | They propose to perform follow-up query analysis to restate context-dependent queries with contextual information. |
| Outcome: | The proposed approach outperforms the state-of-the-art by nearly 8% on the FollowUp dataset . the extensibility of STAR on the SQA dataset is also promising . |
DefGen-Bench: A Benchmark for Chinese Criminal Defence Opinion Generation in LegalAI (2026.acl-long)
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| Challenge: | Existing LegalAI tasks are descriptive or predictive, requiring the users to translate the information into legal reasoning. |
| Approach: | They propose a task to generate a structured defence opinion conditioned jointly on an indictment and the defendant’s stated opinion, which often present conflicting claims. |
| Outcome: | The proposed approach improves on eight large language models (LLMs) and shows that it is more efficient than previous approaches. |
Evaluating the Robustness and Accuracy of Text Watermarking Under Real-World Cross-Lingual Manipulations (2025.findings-emnlp)
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| Challenge: | a study focuses on evaluating watermarking methods for the English language . the literature for evaluating cross-lingual watermarks is scarce . |
| Approach: | They evaluate representative watermarking methods in four different languages . they examine the quality of text under different watermark procedures . |
| Outcome: | The proposed method is compared with other evaluation methods in four different languages. |
Incomplete Utterance Rewriting as Semantic Segmentation (2020.emnlp-main)
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| Challenge: | Recent studies focus on the task of incomplete utterance rewriting as a machine translation task. |
| Approach: | They propose a semantic segmentation task which incorporates edit operations into the problem and predicts a word-level edit matrix. |
| Outcome: | The proposed approach outperforms existing baselines on several datasets and is four times faster than the standard approach in inference. |
CRYPTOGRU: Low Latency Privacy-Preserving Text Analysis With GRU (2021.emnlp-main)
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| Challenge: | Existing approaches to combine HE and GC in RNNs suffer from long inference latency due to the slow activation functions. |
| Approach: | They propose a hybrid structure of HE and GC gated recurrent unit network, for low-latency secure inferences. |
| Outcome: | The proposed structure improves the secure inference latency by up to 138 over one of the state-of-the-art secure networks on the Penn Treebank dataset. |
Leveraging Adjective-Noun Phrasing Knowledge for Comparison Relation Prediction in Text-to-SQL (D19-1)
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| Challenge: | Existing models for text-to-SQL do not explicitly introduce common knowledge to address comparison relations. |
| Approach: | They propose to leverage adjective-noun phrasing knowledge mined from the web to predict comparison relations in text-to-SQL. |
| Outcome: | The proposed approach improves on the original and re-split Spider datasets on comparison relation prediction. |
TrojFSP: Trojan Insertion in Few-shot Prompt Tuning (2024.naacl-long)
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| Challenge: | Prompt tuning on a few data samples presents security issues, e.g., Trojan attacks. |
| Approach: | They propose a method to transfer established data poisoning attacks directly to few-shot prompt tuning, a technique to address the poisoned imbalance issue. |
| Outcome: | The proposed method achieves an ASR of over 99% while maintaining negligible decreases in CDA. |
Awakening Latent Grounding from Pretrained Language Models for Semantic Parsing (2021.findings-acl)
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| Challenge: | Recent years pretrained language models (PLMs) have shown their power on modeling language . however, few efforts have been made to explore grounding capabilities of PLMs . |
| Approach: | They propose to use pretrained language models to explore syntactic structures . they propose to combine their approach with an erasingthen-awakening approach . their results show that the approach can awaken latent grounding, which is understandable to humans . |
| Outcome: | Empirical studies show that the proposed approach can awaken latent grounding . it shows great potential to benefit downstream semantic parsing models, it says . |
Benchmarking Meaning Representations in Neural Semantic Parsing (2020.emnlp-main)
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| Challenge: | Existing work on meaning representations is not comprehensively evaluated due to the lack of readily-available execution engines. |
| Approach: | They propose a unified benchmark on meaning representations by integrating existing semantic parsing datasets, completing the missing logical forms, and implementing the missing execution engines. |
| Outcome: | The proposed benchmark combines existing parsing datasets, completes missing logical forms, and implements missing execution engines. |