Papers by Jiancheng Lv
TIGS: An Inference Algorithm for Text Infilling with Gradient Search (P19-1)
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| Challenge: | Text infilling is an under-explored challenge in the field of text generation. |
| Approach: | They propose an iterative inference algorithm based on gradient search that can be broadly applied to any sequence generative model for text infilling tasks. |
| Outcome: | The proposed method performs well on three different text infilling tasks with different mask ratios and mask strategies compared with five state-of-the-art methods. |
Noisy Pair Corrector for Dense Retrieval (2023.findings-emnlp)
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| Challenge: | Existing dense retrieval models assume that query-document pairs are exactly matched, resulting in mismatched-pair noise. |
| Approach: | They propose a novel approach to train an effective model with mismatched-pair noise. |
| Outcome: | The proposed model performs well on natural question and triviaQA, code-search benchmarks and SO-DS. |
TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance (2021.acl-long)
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Fengbin Zhu, Wenqiang Lei, Youcheng Huang, Chao Wang, Shuo Zhang, Jiancheng Lv, Fuli Feng, Tat-Seng Chua
| Challenge: | Existing QA systems focus on unstructured text, structured knowledge base, or semi-structured tables. |
| Approach: | They propose a large-scale question answering model based on financial reports . numerical reasoning is usually required to infer the answer . |
| Outcome: | The proposed model achieves 58.0% inF1, an 11.1% increase over the baseline model, but still lags behind the best human model. |
Reduce Human Labor On Evaluating Conversational Information Retrieval System: A Human-Machine Collaboration Approach (2023.emnlp-main)
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| Challenge: | Evaluating conversational information retrieval systems requires a significant amount of human labor for annotation. |
| Approach: | They propose to use human annotation to calibrate evaluation results to eliminate evaluation biases. |
| Outcome: | The proposed method consumes less than 1% of human labor and achieves a consistency rate of 95%-99% with human evaluation results. |
How to Enable Effective Cooperation Between Humans and NLP Models: A Survey of Principles, Formalizations, and Beyond (2025.acl-long)
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| Challenge: | Using large language models, intelligent models have evolved into autonomous agents . this paradigm has yielded remarkable progress in numerous NLP tasks in recent years . |
| Approach: | They present a review of human-model cooperation, exploring its principles, formalizations, and open challenges. |
| Outcome: | The proposed model-model cooperation paradigm has been a key focus of recent research . it is a novel paradigm that can be applied to a variety of tasks . |
Unifying Discrete and Continuous Representations for Unsupervised Paraphrase Generation (2023.emnlp-main)
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Mingfeng Xue, Dayiheng Liu, Wenqiang Lei, Jie Fu, Jian Lan, Mei Li, Baosong Yang, Jun Xie, Yidan Zhang, Dezhong Peng, Jiancheng Lv
| Challenge: | Existing unsupervised paraphrase generation methods require large-scale, manually annotated paraphrase datasets, which are labor-intensive to build. |
| Approach: | They propose a self-supervised pseudo-data construction method that generates diverse pseudo-paraphrases in distinct surface structures for a given sentence. |
| Outcome: | The proposed method generates diverse pseudo-paraphrases in distinct surface structures for a given sentence. |
ARAIDA: Analogical Reasoning-Augmented Interactive Data Annotation (2024.acl-long)
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| Challenge: | Empirical studies demonstrate that Araida reduces human correction labor by 11.02% compared to vanilla interactive data annotation methods. |
| Approach: | They propose an analogical reasoning-based approach that enhances automatic annotation accuracy in the interactive data annotation setting and reduces the need for human corrections. |
| Outcome: | Empirical studies show that Araida reduces human correction labor by 11.02% compared to vanilla interactive data annotation methods. |
Reconciliation of Pre-trained Models and Prototypical Neural Networks in Few-shot Named Entity Recognition (2022.findings-emnlp)
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| Challenge: | Existing methods do not consider that pre-trained models contain a prominently large amount of information regarding word frequencies, thus biasing prototypical neural networks against learning word entities. |
| Approach: | They propose a one-line-code normalization method to reconcile such a mismatch with empirical and theoretical grounds and propose 'references' for the model enhancement. |
| Outcome: | The proposed method outperforms the state-of-the-art models on nine benchmark datasets and is comparable to the state of the art. |
Cross-model Transferability among Large Language Models on the Platonic Representations of Concepts (2025.acl-long)
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| Challenge: | Prior work has shown that a single LLM’s concept representations can be captured as steering vectors (SVs) prior work has demonstrated that SVs extracted from smaller LLMs can effectively control the behavior of larger LLM. |
| Approach: | They propose a linear transformation method to bridge LLM concept representations using simple linear transformations to enable efficient cross-model transfer and behavioral control via SVs. |
| Outcome: | The proposed method bridges concept representations across different LLMs and enables efficient cross-model transfer and behavioral control via SVs. |
Create! Don’t Repeat: A Paradigm Shift in Multi-Label Augmentation through Label Creative Generation (2024.naacl-long)
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| Challenge: | Existing methods for multi-label data augmentation have been ineffective, authors say . a mere 1.5% of labels have more than 100 training instances, a problem that persists for years . |
| Approach: | They propose a new paradigm for multi-label data augmentation called Label Creative Generation . they propose tail-driven conditional augmentation with tail-based sampling and label-conditioned generation . |
| Outcome: | The proposed approach has shown a 10% increase in PSP@1 across three datasets . it effectively mitigates the long-tail effect and enhances model performance . |
Can Large Language Models Understand Internet Buzzwords Through User-Generated Content (2025.acl-long)
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| Challenge: | Existing methods for generating definitions of internet buzzwords rely on user-generated content, such as posts and reviews, to understand them. |
| Approach: | They propose a method to generate accurate buzzword definitions using UGC as examples. |
| Outcome: | The proposed method mirrors human language learning skills and can produce more accurate definitions. |
GLGE: A New General Language Generation Evaluation Benchmark (2021.findings-acl)
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Dayiheng Liu, Yu Yan, Yeyun Gong, Weizhen Qi, Hang Zhang, Jian Jiao, Weizhu Chen, Jie Fu, Linjun Shou, Ming Gong, Pengcheng Wang, Jiusheng Chen, Daxin Jiang, Jiancheng Lv, Ruofei Zhang, Winnie Wu, Ming Zhou, Nan Duan
| Challenge: | Multi-task benchmarks focus on a range of Natural Language Understanding (NLU) tasks without considering the Natural Language Generation (NLG) models. |
| Approach: | They propose a multi-task benchmark for evaluating the generalization capabilities of NLG models across eight language generation tasks. |
| Outcome: | The proposed benchmarks are based on GLUE and Su-perGLUE for English and several other languages. |
RikiNet: Reading Wikipedia Pages for Natural Question Answering (2020.acl-main)
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| Challenge: | Using Wikipedia pages to answer open-domain questions remains challenging in natural language understanding. |
| Approach: | They propose a model which reads Wikipedia pages for natural question answering . it uses a dynamic paragraph dual-attention reader and a cascaded answer predictor . |
| Outcome: | The proposed model outperforms the human model on the Natural Questions dataset . it achieves 74.3 F1 and 57.9 F1 on long-answer and short-answer tasks . |
Knowledge Enhanced Pre-training for Cross-lingual Dense Retrieval (2024.lrec-main)
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| Challenge: | Existing mPLMs neglect the importance of knowledge in cross-lingual dense retrieval. |
| Approach: | They propose a novel mPLM that leverages knowledge to learn language-agnostic semantic representations from a multilingual knowledge base and an annotation of Wiki. |
| Outcome: | The proposed model achieves strong multilingual and cross-lingual retrieval performance with significant improvements over existing mPLMs. |
Dynamic Voting for Efficient Reasoning in Large Language Models (2023.findings-emnlp)
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Mingfeng Xue, Dayiheng Liu, Wenqiang Lei, Xingzhang Ren, Baosong Yang, Jun Xie, Yidan Zhang, Dezhong Peng, Jiancheng Lv
| Challenge: | Multi-path voting methods generate multiple reasoning paths for each problem, causing factual errors and illusion generation. |
| Approach: | They propose a multi-path voting technique that effectively reduces the number of reasoning paths during multi-path voting while preserving accuracies. |
| Outcome: | The proposed method outperforms Self-consistency using 24.7% of the number of paths on the LetterConcat task. |
Knowing-how & Knowing-that: A New Task for Machine Comprehension of User Manuals (2023.findings-acl)
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| Challenge: | Existing methods for machine reading comprehension of user manuals have trouble answering complex questions. |
| Approach: | They propose a knowing-how & knowing-that task that requires the model to answer factoid-style, procedure-style and inconsistent questions about user manuals. |
| Outcome: | The proposed model can answer factoid-style, procedure-style and inconsistent questions about user manuals. |
Selective Annotation via Data Allocation: These Data Should Be Triaged to Experts for Annotation Rather Than the Model (2024.findings-emnlp)
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| Challenge: | Existing methods to obtain high-quality annotations under limited budgets focus on selecting informative data for expert annotations while the rest of the data is assigned to model annotation. |
| Approach: | They propose a semi-automatic annotation framework that uses error-aware triage and bi-weighting mechanisms to obtain high-quality annotations under limited budget. |
| Outcome: | The proposed framework outperforms baselines in the data annotation problem under limited budgets. |
Diverse, Controllable, and Keyphrase-Aware: A Corpus and Method for News Multi-Headline Generation (2020.emnlp-main)
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| Challenge: | Existing methods for news headline generation focus on producing a single short sentence . et al., 2017; Gehrmann e.t., 2018; Zhong ee., 2019) focus on single-headline generation. |
| Approach: | They propose a method to generate multiple headlines with keyphrases of user interests . they propose generating multiple keyphrase-relevant headlines using a transformer decoder . |
| Outcome: | The proposed method achieves state-of-the-art in terms of quality and diversity. |
POS-Constrained Parallel Decoding for Non-autoregressive Generation (2021.acl-long)
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| Challenge: | Existing non-autoregressive generation systems face multimodality problem due to conditionally independent decoding. |
| Approach: | They propose to incorporate linguistic structure into NAG inference instead of teacher AG . they propose a method that provides a specific POS sequence to constrain the NAG model . |
| Outcome: | The proposed method improves NAG models on four text generation tasks to a greater extent compared to knowledge distillation. |
Tell Me How to Ask Again: Question Data Augmentation with Controllable Rewriting in Continuous Space (2020.emnlp-main)
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| Challenge: | Existing data augmentation techniques for natural language processing tasks are difficult to design. |
| Approach: | They propose a controllable rewriting based question data augmentation method for machine reading comprehension, question generation and question-answering natural language inference tasks. |
| Outcome: | The proposed method generates high-quality, high-quality question data samples on machine reading comprehension, question generation, and question-answering natural language inference tasks. |