Papers by Jian-Guang Lou
Question Answering as Programming for Solving Time-Sensitive Questions (2023.emnlp-main)
Copied to clipboard
| Challenge: | Recent studies show that Large Language Models (LLMs) have shown remarkable intelligence in question answering. |
| Approach: | They propose to reframe the Question Answering task as Programming to overcome this limitation by leveraging LLMs' superior ability in understanding both natural language and programming language. |
| Outcome: | The proposed approach improves on time-sensitive question answering datasets by 14.5% over baselines. |
Towards Robustness of Text-to-SQL Models Against Natural and Realistic Adversarial Table Perturbation (2022.acl-long)
Copied to clipboard
| Challenge: | Existing Text-to-SQL parsers are vulnerable to perturbations in NL questions . we propose the Adversarial Table Perturbation (ATP) as a new attacking paradigm . |
| Approach: | They propose to use the Adversarial Table Perturbation to measure robustness of Text-to-SQL parsers against adversarial perturbations. |
| Outcome: | The proposed approach outperforms baseline methods in robustness evaluations on ADVETA and can be used in future projects. |
E5: Zero-shot Hierarchical Table Analysis using Augmented LLMs via Explain, Extract, Execute, Exhibit and Extrapolate (2024.naacl-long)
Copied to clipboard
| Challenge: | Hierarchical tables with multi-level headers are confusing for models due to their complex structure, implicit semantics, and calculation relationships. |
| Approach: | They propose a code-augmented framework for zero-shot hierarchical table question answering that combines self-explaining the table's hierarchic structures with external code execution to prevent hallucinations. |
| Outcome: | The proposed framework outperforms state-of-the-art fine-tuning methods with a 44.38 Exact Match improvement. |
CRT-QA: A Dataset of Complex Reasoning Question Answering over Tabular Data (2023.emnlp-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) show powerful reasoning abilities on text-based tasks, but their reasoning capability on structured data such as tables has not been systematically explored. |
| Approach: | They first establish a comprehensive taxonomy of reasoning and operation types for tabular data analysis and then construct a complex reasoning QA dataset over tabular dataset. |
| Outcome: | The proposed method is able to solve table reasoning tasks without handcrafted demonstrations. |
Making Language Models Better Reasoners with Step-Aware Verifier (2023.acl-long)
Copied to clipboard
| Challenge: | Large language models have made impressive progress in few-shot learning but still face difficulties in reasoning tasks such as GSM8K. |
| Approach: | They propose a new approach that uses a verifier to filter out incorrect answers based on a weighted voting scheme to improve reasoning ability of language models. |
| Outcome: | The proposed approach improves GSM8K reasoning rate by 17.9% to 58.1%. |
“What Do You Mean by That?” A Parser-Independent Interactive Approach for Enhancing Text-to-SQL (2020.emnlp-main)
Copied to clipboard
| 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)
Copied to clipboard
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 . |
Can LLMs Learn From Mistakes? An Empirical Study on Reasoning Tasks (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing work has shown that simple learning can enhance the chain-of-thought (CoT) reasoning of large language models. |
| Approach: | They construct mistake-correction datasets to identify and correct mistakes in CoTs . they conclude that LLMs can learn from mistakes to enhance their CoT reasoning . |
| Outcome: | The proposed datasets show that LLMs can learn from mistakes to enhance their CoT reasoning performance. |
Data-Anonymous Encoding for Text-to-SQL Generation (D19-1)
Copied to clipboard
| Challenge: | Existing approaches to handle table-related tokens before the semantic parser are not efficient . existing approaches ignore handling table- related tokens or use deterministic approaches based on string-match or word embedding similarity. |
| Approach: | They propose a more efficient approach to handle table-related tokens before the parser . they propose tagging a sequential tabbing problem and an implicit supervision approach . |
| Outcome: | The proposed approach significantly outperforms deterministic approaches. |
TWT: Table with Written Text for Controlled Data-to-Text Generation (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods output hallucinated text that is not faithful on TWT. |
| Approach: | They propose to generate text conditioned on the structured data and a prefix by leveraging pre-trained neural models. |
| Outcome: | The proposed approach outperforms state-of-the-art methods under automatic and human evaluation metrics. |
LEMON: Language-Based Environment Manipulation via Execution-Guided Pre-training (2022.findings-emnlp)
Copied to clipboard
| 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. |
AdapterShare: Task Correlation Modeling with Adapter Differentiation (2022.emnlp-main)
Copied to clipboard
| Challenge: | AdapterShare is an adapter differentiation method to explicitly model the task correlation among multiple tasks. |
| Approach: | They propose an adapter differentiation method to explicitly model the task correlation among multiple tasks. |
| Outcome: | The proposed method achieves 1.90 points improvement on five dialogue understanding tasks and 2.33 points gain on NLU tasks. |
Towards Knowledge-Intensive Text-to-SQL Semantic Parsing with Formulaic Knowledge (2022.emnlp-main)
Copied to clipboard
Longxu Dou, Yan Gao, Xuqi Liu, Mingyang Pan, Dingzirui Wang, Wanxiang Che, Dechen Zhan, Min-Yen Kan, Jian-Guang Lou
| Challenge: | Existing approaches to text-to-SQL require domain knowledge to parse expert questions into SQL queries. |
| Approach: | They propose a framework to leverage domain knowledge during parsing by building a new benchmark KnowSQL with domain-specific questions. |
| Outcome: | The proposed framework improves the performance of the proposed benchmark by 28.2%. |
Exploring the Secrets Behind the Learning Difficulty of Meaning Representations for Semantic Parsing (2022.emnlp-main)
Copied to clipboard
| 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. |
When Language Model Meets Private Library (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing language models have been pre-trained on large-scale code corpora and generate decent code snippets. |
| Approach: | They propose a framework that can provide pre-trained language models with the ability to generate code using private libraries. |
| Outcome: | The proposed framework can generate code using private libraries using off-the-shelf language models or pre-trained models on code corpus containing API information. |
StraGo: Harnessing Strategic Guidance for Prompt Optimization (2024.findings-emnlp)
Copied to clipboard
Yurong Wu, Yan Gao, Bin Zhu, Zineng Zhou, Xiaodi Sun, Sheng Yang, Jian-Guang Lou, Zhiming Ding, Linjun Yang
| Challenge: | Existing methods for prompt optimization often lead to prompt drifting, wherein newly generated prompts canadversely impact previously successful cases while addressing failures. |
| Approach: | They propose a method to mitigate prompt drifting by integrating in-context learning to formulate specific, actionable strategies for prompt optimization. |
| Outcome: | The proposed approach mitigates prompt drifting by leveraging insights from both successful and failed cases to identify critical factors for achieving optimization objectives. |
TACR: A Table Alignment-based Cell Selection Method for HybridQA (2023.findings-acl)
Copied to clipboard
| Challenge: | Hybrid Question-Answering datasets lack a robust reasoning model for text-based QA. |
| Approach: | They propose a table-question-alignment-based cell-selection and reasoning model for hybrid text and table QA. |
| Outcome: | The proposed model outperforms baselines on HybridQA and WikiTableQuestions datasets on cell selection and argumentation. |
ReTraCk: A Flexible and Efficient Framework for Knowledge Base Question Answering (2021.acl-demo)
Copied to clipboard
| 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. |
Learning Algebraic Recombination for Compositional Generalization (2021.findings-acl)
Copied to clipboard
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. |
Re-Reading Improves Reasoning in Large Language Models (2024.emnlp-main)
Copied to clipboard
| Challenge: | Unlike thought-eliciting prompting methods, RE2 shifts the focus to the input by processing questions twice, thereby enhancing the understanding process. |
| Approach: | They introduce a simple, yet general and effective prompting method, RE2, which rereads the question as input. |
| Outcome: | The proposed method demonstrates strong generality and compatibility with most thought-eliciting prompting methods, including CoT. |
Large Language Models Meet NL2Code: A Survey (2023.acl-long)
Copied to clipboard
Daoguang Zan, Bei Chen, Fengji Zhang, Dianjie Lu, Bingchao Wu, Bei Guan, Wang Yongji, Jian-Guang Lou
| Challenge: | generating code from a natural language description is a pressing and significant challenge in code intelligence. |
| Approach: | They propose to survey 27 existing large language models for NL2Code and compare them to humanEval benchmarks. |
| Outcome: | The proposed model is compared with existing models on the HumanEval benchmark. |
You Impress Me: Dialogue Generation via Mutual Persona Perception (2020.acl-main)
Copied to clipboard
| 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. |
HiTab: A Hierarchical Table Dataset for Question Answering and Natural Language Generation (2022.acl-long)
Copied to clipboard
Zhoujun Cheng, Haoyu Dong, Zhiruo Wang, Ran Jia, Jiaqi Guo, Yan Gao, Shi Han, Jian-Guang Lou, Dongmei Zhang
| Challenge: | Existing studies on table reasoning focus on flat tables and hierarchical tables . a new dataset, HiTab, aims to examine numerical reasoning over hierarchic tables based on hierarchically structured tables - a strong challenge for existing baselines and a valuable benchmark for future research. |
| Approach: | They propose a hierarchical question answering and natural language generation dataset to study hierarchic tables. |
| Outcome: | The proposed model shows that it is effective in QA and natural language generation over hierarchical tables. |
Translating Headers of Tabular Data: A Pilot Study of Schema Translation (2021.emnlp-main)
Copied to clipboard
| Challenge: | Schema translation is not well studied in the community because of morphological difference and context difference between plain text and tabular data. |
| Approach: | They propose a schema translation model augmented with schema context . they model a target header and its context as a directed graph to represent their entities . |
| Outcome: | The proposed model outperforms state-of-the-art models on schema translation . it uses a graph to represent entity types and relations, and a relational-aware transformer . |
Skill-Based Few-Shot Selection for In-Context Learning (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods based on pre-trained embeddings can be easily biased by surface features that are not important for the target task. |
| Approach: | They propose a skill-based few-shot selection method for in-context learning . it generates skill-specific descriptions for each test case and candidate example . |
| Outcome: | The proposed method significantly outperforms existing methods in five cross-domain semantic parsing datasets and six backbone models. |
Chase: A Large-Scale and Pragmatic Chinese Dataset for Cross-Database Context-Dependent Text-to-SQL (2021.acl-long)
Copied to clipboard
| 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. |
Know What I don’t Know: Handling Ambiguous and Unknown Questions for Text-to-SQL (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing text-to-SQL parsers generate a plausible SQL query for arbitrary user questions, thereby failing to handle problematic user questions. |
| Approach: | They propose a weakly supervised DTE model for error detection, localization, and explanation. |
| Outcome: | The proposed model achieves the best result on real-world examples and generated examples compared with baselines. |
Uncovering and Categorizing Social Biases in Text-to-SQL (2023.acl-long)
Copied to clipboard
| Challenge: | Existing Text-to-SQL models are trained on clean, neutral datasets, such as Spider and WikiSQl, but these models contain social bias at different rates. |
| Approach: | They propose to use data to map natural language utterances to SQL queries. |
| Outcome: | The proposed model can contain social bias at different rates in the downstream Text-to-SQL task. |
AMPO: Automatic Multi-Branched Prompt Optimization (2024.emnlp-main)
Copied to clipboard
Sheng Yang, Yurong Wu, Yan Gao, Zineng Zhou, Bin Zhu, Xiaodi Sun, Jian-Guang Lou, Zhiming Ding, Anbang Hu, Yuan Fang, Yunsong Li, Junyan Chen, Linjun Yang
| Challenge: | Existing prompt engineering techniques are limited to producing single flow instructions, struggling with handling diverse patterns. |
| Approach: | They propose an automatic prompt optimization method that iteratively develops a multi-branched prompt using failure cases as feedback. |
| Outcome: | The proposed method achieves the best results across five tasks and demonstrates significant optimization efficiency due to adoption of a minimal search strategy. |
Weakly Supervised Semantic Parsing by Learning from Mistakes (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Weakly supervised semantic parsing requires searching consistent logical forms in a huge space and dealing with spurious logical form. |
| Approach: | They propose a learning framework that trains parsers via utterance-denotation pairs . they use utterrance-logical form pairs created from mistakes to bootstrap parser . |
| Outcome: | The proposed framework outperforms state-of-the-art methods on WikiSQL, TabFact and other datasets. |
A Split-and-Recombine Approach for Follow-up Query Analysis (D19-1)
Copied to clipboard
| 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 . |
Single-/Multi-Source Cross-Lingual NER via Teacher-Student Learning on Unlabeled Data in Target Language (2020.acl-main)
Copied to clipboard
| Challenge: | Existing approaches to named entity recognition (NER) are limited by label projection with pairwise texts or direct model transfer. |
| Approach: | They propose a method where NER models in the source languages are used as teachers to train a student model on unlabeled data in the target language. |
| Outcome: | The proposed method outperforms existing state-of-the-art methods for single-source and multi-source cross-lingual NER on target languages. |
Incomplete Utterance Rewriting as Semantic Segmentation (2020.emnlp-main)
Copied to clipboard
| 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. |
SemRegex: A Semantics-Based Approach for Generating Regular Expressions from Natural Language Specifications (D18-1)
Copied to clipboard
| Challenge: | Existing approaches to generate programs from natural language do not address program aliasing . semantically equivalent programs may have many syntactically different forms . |
| Approach: | They propose a semantics-based approach to generate regular expressions from natural language. |
| Outcome: | The proposed approach improves on three public datasets. |
Automatic Instruction Evolving for Large Language Models (2024.emnlp-main)
Copied to clipboard
| Challenge: | Evol-Instruct is an end-to-end framework that evolves instruction datasets without human effort. |
| Approach: | They propose an end-to-end framework that evolves instruction datasets without human effort by analyzing and analyzing evolutionary strategies for the given instruction data. |
| Outcome: | The proposed method outperforms human-designed methods on various benchmarks including MT-Bench, AlpacaEval, GSM8K, and HumanEval. |
Leveraging Adjective-Noun Phrasing Knowledge for Comparison Relation Prediction in Text-to-SQL (D19-1)
Copied to clipboard
| 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. |
GL-CLeF: A Global–Local Contrastive Learning Framework for Cross-lingual Spoken Language Understanding (2022.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to zero-shot cross-lingual spoken language understanding rely on shared parameters, which can only perform implicit alignment across languages. |
| Approach: | They propose a global-local contrastive learning framework to achieve a fine-grained cross-lingual transfer . they employ bilingual dictionaries to construct multilingual views of the same utterance . |
| Outcome: | Experiments on MultiATIS++ show that GL-CLeF achieves the best performance . GL is based on dictionaries and encourages representations to be more similar than negative example pairs . |
How Do In-Context Examples Affect Compositional Generalization? (2023.acl-long)
Copied to clipboard
| Challenge: | In-context learning paradigms that focus on large corpus are limiting compositional generalization performance. |
| Approach: | They propose a test suite to investigate in-context compositional generalization . they propose to use examples that are structurally similar to the test case . |
| Outcome: | The proposed test suite investigates in-context compositional generalization performance . it finds that the performance can be affected by the selection of in-const examples . |
RepoCoder: Repository-Level Code Completion Through Iterative Retrieval and Generation (2023.emnlp-main)
Copied to clipboard
Fengji Zhang, Bei Chen, Yue Zhang, Jacky Keung, Jin Liu, Daoguang Zan, Yi Mao, Jian-Guang Lou, Weizhu Chen
| Challenge: | RepoCoder is a repository-level code completion framework that utilizes the useful information scattered in files. |
| Approach: | They propose a repository-level code completion framework called RepoCoder . it integrates a similarity-based retriever and a pre-trained code language model . they propose 'repoBench' benchmark to validate the framework's effectiveness . |
| Outcome: | The proposed framework outperforms the vanilla retrieval-augmented code completion approach in the real-world. |
Awakening Latent Grounding from Pretrained Language Models for Semantic Parsing (2021.findings-acl)
Copied to clipboard
| 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 . |
Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representation (P19-1)
Copied to clipboard
| Challenge: | IRNet synthesizes SQL queries in an end-to-end manner, but it yields unsatisfactory performance on public benchmarks. |
| Approach: | They propose a neural approach called IRNet for complex and cross-domain Text-to-SQL. |
| Outcome: | IRNet achieves 46.7% accuracy on the Spider benchmark, a 19.5% improvement over state-of-the-art approaches. |
Benchmarking Meaning Representations in Neural Semantic Parsing (2020.emnlp-main)
Copied to clipboard
| 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. |