Papers by Liqiang Zhang
Learning What to Share: Leaky Multi-Task Network for Text Classification (C18-1)
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| Challenge: | Existing approaches to multi-task learning suffer from the interference between tasks because they lack selection mechanism for feature sharing. |
| Approach: | They propose a multi-task convolutional neural network with the Leaky Unit which has memory and forgetting mechanism to filter the feature flows between tasks. |
| Outcome: | The proposed model can filter feature flows between tasks and improve performance on five datasets. |
Do LLMs Catch Their Own Mistakes? A Comprehensive Benchmark for Reflective Tool Use LLMs (2026.findings-acl)
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| Challenge: | Existing benchmarks primarily evaluate planning and execution success, overlooking the self-reflective dimension of tool use. |
| Approach: | They propose a benchmark to assess LLMs’ self-reflective reasoning in tool-augmented multi-turn dialogues. |
| Outcome: | The proposed benchmark covers 10 domains with 88 distinct APIs and 968 annotated dialogues, systematically injecting diverse error types arising from both user and assistant behavior. |
An Empirical Study of Frame Selection for Text-to-Video Retrieval (2023.findings-emnlp)
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| Challenge: | Existing methods for text-to-video retrieval select a subset of frames to represent video content . current methods only explore video contents while ignoring relevancy to texts . |
| Approach: | They propose to use a subset of frames to represent video content for TVR . they analyze six different frame selection methods to determine their effectiveness . |
| Outcome: | The proposed method improves retrieval efficiency without sacrificing visual details . the proposed method explores the video contents while ignoring relevancy to texts . |
Gated Multi-Task Network for Text Classification (N18-2)
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| Challenge: | Existing approaches to multitask learning share the features without distinguishing the usefulness of the features, generating undesired interference between tasks. |
| Approach: | They propose to introduce a gate mechanism into multi-task CNN and propose a new gated sharing unit which can filter the feature flows between tasks and greatly reduce the interference. |
| Outcome: | The proposed approach can learn selection rules automatically and gain a great improvement over strong baselines. |
ORPP: Self-Optimizing Role-playing Prompts to Enhance Language Model Capabilities (2025.emnlp-main)
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| Challenge: | Existing research has explored model-driven strategies for prompt optimization, but these methods suffer from high computational overhead or require strong optimization capabilities from the model itself, which limits their broad applicability. |
| Approach: | They propose a framework that optimizes and generates role-playing prompts by limiting the prompt search space to role-player scenarios. |
| Outcome: | The proposed framework matches and surpasses existing prompt optimization methods in terms of performance. |
MCapsNet: Capsule Network for Text with Multi-Task Learning (D18-1)
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| Challenge: | Multi-task learning has been frustrated by the interference among tasks. |
| Approach: | They propose a capsule-based multi-task learning architecture which is unified, simple and effective. |
| Outcome: | The proposed model can cluster features for each task in the network, which helps reduce the interference among tasks. |
Make Imagination Clearer! Stable Diffusion-based Visual Imagination for Multimodal Machine Translation (2025.acl-long)
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Andong Chen, Yuchen Song, Kehai Chen, Xuefeng Bai, Muyun Yang, Liqiang Nie, Jie Liu, Tiejun Zhao, Min Zhang
| Challenge: | Experimental results show that our model significantly outperforms existing multimodal MT and text-only MT. |
| Approach: | They propose a stable diffusion-based imagination network into a multimodal large language model to generate an image for each source sentence. |
| Outcome: | The proposed model outperforms existing multimodal and text-only MT and achieves an average improvement of 14 BLEU points on Multi30K and MSCOCO multimodal MT benchmarks. |
VCB Bench: An Evaluation Benchmark for Audio-Grounded Large Language Model Conversational Agents (2026.findings-acl)
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Jiliang Hu, Wenfu Wang, Zuchao Li, Chenxing Li, Yiyang Zhao, Hanzhao Li, Liqiang Zhang, Meng Yu, Dong Yu
| Challenge: | despite advances in multimodal conversational systems, current benchmarks lack comprehensive evaluation across key dimensions. |
| Approach: | They propose a Chinese benchmark built exclusively on real human speech to fill this gap . they assess LALMs across three complementary axes: instruction following, knowledge understanding, robustness . |
| Outcome: | VCB Bench assesses LALMs across three complementary axes: instruction following, knowledge understanding, and robustness . VCBM Bench provides reproducible and fine-grained framework for Chinese voice chat bots . results show significant performance disparities and offer tangible insights for future improvements . |
LRQuant: Learnable and Robust Post-Training Quantization for Large Language Models (2024.acl-long)
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| Challenge: | Existing methods for post-training quantization (PTQ) are limited by the complexity of the quantization parameter and performance degradations when tested on unseen datasets. |
| Approach: | They propose a learnable smooth-based PTQ framework that allows for rapid adaptation during testing. |
| Outcome: | The proposed framework improves performance on unseen datasets and reduces memory constraints. |
End-to-End Conversational Search for Online Shopping with Utterance Transfer (2021.emnlp-main)
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Liqiang Xiao, Jun Ma, Xin Luna Dong, Pascual Martínez-Gómez, Nasser Zalmout, Chenwei Zhang, Tong Zhao, Hao He, Yaohui Jin
| Challenge: | a new study proposes a conversational search system that integrates product attributes and dialog with search . but it faces two real world challenges: imperfect product schema/knowledge and lack of training dialog data . |
| Approach: | They propose an end-to-end conversational search system that integrates search with text . they propose an utterance transfer approach that generates dialogue utterations from other domains . |
| Outcome: | The proposed system outperforms the best tested baseline in a conversational search dataset for online shopping. |
GUI-explorer: Autonomous Exploration and Mining of Transition-aware Knowledge for GUI Agent (2025.acl-long)
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| Challenge: | GUI automation is a key challenge in dynamic environments. |
| Approach: | They propose a training-free GUI agent that integrates two mechanisms to explore trajectories in GUIs. |
| Outcome: | The proposed GUI-explorer shows significant improvements over existing agents. |
CoRe-MMRAG: Cross-Source Knowledge Reconciliation for Multimodal RAG (2025.acl-long)
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| Challenge: | Recent advances in Multimodal Large Language Models have significantly improved reasoning and generation tasks by leveraging joint vision-language representations. |
| Approach: | They propose a framework that reconciles inconsistencies across knowledge sources . they use a four-stage pipeline to generate an internal response from parametric knowledge . |
| Outcome: | Experiments on KB-VQA show that CoRe-MMRAG achieves performance gains of 5.6% and 9.3% over baseline methods. |
Rule-KBQA: Rule-Guided Reasoning for Complex Knowledge Base Question Answering with Large Language Models (2025.coling-main)
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| Challenge: | Existing methods for knowledge base question answering lack grammaticality, faithfulness, and controllability due to hallucinations in the reasoning process. |
| Approach: | They propose a framework that employs learned rules to guide the generation of logical forms. |
| Outcome: | The proposed method achieves competitive results on standard KBQA datasets. |
TIU-Bench: A Benchmark for Evaluating Large Multimodal Models on Text-rich Image Understanding (2025.findings-emnlp)
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| Challenge: | Existing text-rich image understanding benchmarks lack scale and fragmented scenarios . a new full-image structured output format is proposed to enable fine-grained evaluation of perception and reasoning capabilities. |
| Approach: | They propose a large-scale, multilingual benchmark that includes over 100,000 annotations and 22,000 question-answer pairs. |
| Outcome: | The proposed framework provides a comprehensive platform for developing and evaluating next-generation multimodal AI systems. |
Weight-Aware Activation Sparsity with Constrained Bayesian Optimization Scheduling for Large Language Models (2025.emnlp-main)
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| Challenge: | Existing activation sparsification methods rely on activation magnitude and weights for sparsity . authors propose a weight-aware activation-a-ware framework for large language models . |
| Approach: | They propose a weight-aware activation sparsity framework that uses weight-based scoring to measure activation importance in sparsification and a custom GPU sparse kernel to support it. |
| Outcome: | The proposed framework outperforms existing methods at 60% model-level sparsity and significantly outperfies them at higher sparsities. |
Efficient Safety Alignment of Large Language Models via Preference Re-ranking and Representation-based Reward Modeling (2025.acl-long)
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| Challenge: | Existing safety alignment methods for Large Language Models (LLMs) face the distribution shift issue, which requires significant computational resources. |
| Approach: | They propose a framework that leverages the model’s intrinsic safety judgment capability to extract reward signals, which are then used to calculate label confidence for preference reordering. |
| Outcome: | The proposed framework improves safety performance while avoiding 300x computational overheads. |
Multi-Task Label Embedding for Text Classification (D18-1)
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| Challenge: | Existing work treats labels of each task as independent and meaningless one-hot vectors, which cause a loss of potential label information. |
| Approach: | They propose to combine multi-task learning with semantic vectors to convert labels into vectors . their results are based on extensive experiments on five benchmark datasets based in chinese . |
| Outcome: | The proposed model can improve performance on five benchmark datasets on text classification tasks. |
HAIC: Improving Human Action Understanding and Generation with Better Captions for Multi-modal Large Language Models (2025.acl-long)
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Xiao Wang, Jingyun Hua, Weihong Lin, Yuanxing Zhang, Fuzheng Zhang, Jianlong Wu, Di Zhang, Liqiang Nie
| Challenge: | Existing studies have shown that high-quality video captions can improve MLLMs' performance on videos involving human actions. |
| Approach: | They propose a data annotation pipeline to collect videos featuring clear human actions from the Internet and annotate them in a standardized caption format that uses human attributes to distinguish individuals. |
| Outcome: | The proposed pipeline combines two datasets to evaluate human action understanding. |
Improving Distantly-Supervised Relation Extraction with Joint Label Embedding (D19-1)
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| Challenge: | Existing methods for relation extraction treat labels as independent and meaningless one-hot vectors, which cause a loss of potential label information for selecting valid instances. |
| Approach: | They propose a multi-layer attention-based model to improve relation extraction with joint label embedding by gating integration and using the embeddable entities as an atten- tion. |
| Outcome: | The proposed model significantly outperforms state-of-the-art methods in relation extraction with joint label embedding. |
Translatotron-V(ison): An End-to-End Model for In-Image Machine Translation (2024.findings-acl)
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| Challenge: | In-image machine translation (IIMT) aims to translate an image containing texts in source language into an image with translations in target language. |
| Approach: | They propose an end-to-end IIMT model with four modules that translate images . they propose a two-stage training framework to assist the model in learning alignment across languages . |
| Outcome: | The proposed model outperforms cascaded models with only 70.9% of parameters and is highly accurate. |
Evolving Sparsity: Leveraging Token Importance Dynamics for Efficient LLM Decoding with Sparse Attention (2026.acl-long)
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| Challenge: | Efficient long-context inference remains a major challenge for large language models (LLMs), as the cost of attention computation during auto-regressive decoding grows linearly with the context length. |
| Approach: | They propose to model token importance as a dynamic process that evolves over decoding steps and propagates through model layers. |
| Outcome: | The proposed method outperforms baseline sparse attention methods and achieves speedups of up to 5.36 for attention latency and 2.33 for end-to-end decoding. |
LMR-BENCH: Evaluating LLM Agent’s Ability on Reproducing Language Modeling Research (2025.emnlp-main)
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Shuo Yan, Ruochen Li, Ziming Luo, Zimu Wang, Daoyang Li, Liqiang Jing, Kaiyu He, Peilin Wu, Juntong Ni, George Michalopoulos, Yue Zhang, Ziyang Zhang, Mian Zhang, Zhiyu Chen, Xinya Du
| Challenge: | Large language model (LLM) agents have demonstrated remarkable potential in advancing scientific discovery, but their capability in reproducing code from research papers remains underexplored. |
| Approach: | They propose to evaluate LLM agents' ability to reproduce scientific research papers by analyzing code reproduction tasks from 23 research papers published in top-tier NLP venues. |
| Outcome: | The proposed benchmark systematically evaluates the capability of large language model (LLM) agents on code reproduction from Language Modeling Research. |
Dually Self-Improved Counterfactual Data Augmentation Using Large Language Model (2025.acl-long)
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| Challenge: | Existing approaches to generate counterfactual data augmentation are limited due to imbalance and biases in real-world training data. |
| Approach: | They propose a self-improved method for generating high-quality counterfacts using large language models. |
| Outcome: | The proposed method generates high-quality counterfacts on the natural language inference task using lightweight and task-specific LLMs. |
FIHA: Automated Fine-grained Hallucinations Evaluations in Large Vision Language Models with Davidson Scene Graphs (2025.findings-acl)
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| Challenge: | Current approaches to large vision-language models rely on costly annotations and are not comprehensive in terms of evaluating all aspects. |
| Approach: | They propose an automated method which can access LVLMs hallucination in an LLM-free and annotation-free way and model the dependency between different types of halluciNations. |
| Outcome: | The proposed model can model the dependency between different types of hallucinations and generate Q&A pairs on any image dataset at minimal cost. |
Distillation Enhanced Generative Retrieval (2024.findings-acl)
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| Challenge: | Generative retrieval is a promising new paradigm in text retrieval that generates identifier strings of relevant passages as the retrieval target. |
| Approach: | They propose a framework that leverages generative language models to enhance generative retrieval by distillation. |
| Outcome: | The proposed framework achieves state-of-the-art performance among the generative retrieval methods. |
Bi-Tuning with Collaborative Information for Controllable LLM-based Sequential Recommendation (2025.acl-long)
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| Challenge: | Existing approaches to optimize sequential recommendation systems rely on item ID sequences, but they lack collaborative knowledge and limited controllability. |
| Approach: | They propose a simple bi-tuning framework with collaborative information for controllable Large Language Model-based Sequential Recommendation (Laser) they incorporate learnable virtual tokens at prefix and suffix of input text to adapt LLMs with collaborative knowledge . |
| Outcome: | The proposed framework outperforms state-of-the-art recommendations on real-world datasets. |