Papers by Xuxin Cheng
MRRL: Modifying the Reference via Reinforcement Learning for Non-Autoregressive Joint Multiple Intent Detection and Slot Filling (2023.findings-emnlp)
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| Challenge: | Existing non-autoregressive models for multiple intent detection and slot filling have limited overall accuracy due to multi-modality problem and lack of alignment between correct predictions. |
| Approach: | They propose a method for multiple intent detection and slot filling that introduces a modifier and employs reinforcement learning to modify the reference. |
| Outcome: | The proposed method outperforms the previous best approach by 3.6 overall accuracy on MixATIS dataset. |
SILO-BENCH: A Scalable Environment for Evaluating Distributed Coordination in Multi-Agent LLM Systems (2026.acl-long)
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Yuzhe Zhang, Feiran Liu, Yi Shan, Xinyi Huang, Xin Yang, Yueqi Zhu, Xuxin Cheng, Cao Liu, Ke Zeng, Terry Jingchen Zhang, Wenyuan Jiang
| Challenge: | Existing benchmarks conflate coordination ability with role-based priors. |
| Approach: | They propose a role-free benchmark for evaluating free-form collaboration under information silos. |
| Outcome: | The proposed benchmark systematically probes coordination capabilities under information silos using 54 configurations and 3 frontier LLMs. |
Cyclical Contrastive Learning Based on Geodesic for Zero-shot Cross-lingual Spoken Language Understanding (2024.findings-acl)
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| Challenge: | zero-shot cross-lingual SLU is a challenging task in low-resource languages . a lack of labeled training data makes it difficult to align representations of similar sentences . |
| Approach: | They propose a framework that uses cyclical contrastive learning to achieve consistency between languages . they propose to use geodesic to measure the similarity to construct positive and negative pairs . |
| Outcome: | The proposed framework achieves state-of-the-art performance on multiATIS++ and MTOP datasets. |
Code-Switching Can be Better Aligners: Advancing Cross-Lingual SLU through Representation-Level and Prediction-Level Alignment (2024.acl-short)
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| Challenge: | Existing code-switching-based cross-lingual spoken language understanding frameworks are limited to low-resource languages. |
| Approach: | They propose a cross-lingual spoken language understanding framework that leverages both code-switched and original sentences to achieve multi-level alignment. |
| Outcome: | The proposed framework can achieve multi-level alignment on two benchmarks across ten languages. |
Knowledge-enhanced Prompt Tuning for Dialogue-based Relation Extraction with Trigger and Label Semantic (2024.lrec-main)
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| Challenge: | Existing methods to determine semantic relation between two arguments in dialogues are limited due to the low information density of text. |
| Approach: | They propose a Knowledge-Enhanced Prompt-Tuning method to enhance DRE model by exploiting trigger and label semantics. |
| Outcome: | The proposed method achieves state-of-the-art in F1 and F1c scores on a DialogRE dataset. |
Learning to Match Representations is Better for End-to-End Task-Oriented Dialog System (2024.findings-emnlp)
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| Challenge: | Existing systems for task-oriented dialogue lack belief states as supervisory signals. |
| Approach: | They propose a method for knowledge retrieval driven by matching representations . they use a matching signal extractor to extract matching representation between contexts and entities . |
| Outcome: | Experiments on three standard benchmarks show that the proposed method performs better than existing approaches. |
Accelerating Multiple Intent Detection and Slot Filling via Targeted Knowledge Distillation (2023.findings-emnlp)
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| Challenge: | Existing non-autoregressive Spoken Language Understanding models suffer from multi-modality problem . current methods have little prior knowledge about the reference during inference . |
| Approach: | They propose a Targeted Knowledge Distillation Framework (TKDF) for multi-intent SLU that utilizes the knowledge distillation method to improve the performance. |
| Outcome: | The proposed model outperforms existing models on two public multi-intent datasets while speeding up by over 4.5 times. |
DIFFA-2: A Practical Diffusion Large Language Model for General Audio Understanding (2026.findings-acl)
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| Challenge: | Autoregressive (AR) large audio language models are expensive in data and computation . prior work shows diffusion-based LALMs can improve audio understanding under matched settings . |
| Approach: | They propose a diffusion-based LALM that upgrades the speech encoder and employs dual semantic and acoustic adapters. |
| Outcome: | a new model improves over existing autoregressive large language models and is competitive to strong AR models . the proposed model can make use of limited training data and improve inference efficiency . a recent study shows that diffusion-based models can improve audio understanding . |
MaCSC: Towards Multimodal-augmented Pre-trained Language Models via Conceptual Prototypes and Self-balancing Calibration (2024.naacl-long)
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| Challenge: | Existing approaches to training pre-trained language models (PLMs) focus on static image modality; inevitably encounter modality gaps and noise; and treat all modalities. |
| Approach: | They propose a multimodal-augmented framework that can infuse multimodal semantics into PLMs and facilitate a self-balancing calibration of information allocation. |
| Outcome: | The proposed framework outperforms baselines on multiple NLP tasks and outperformed existing frameworks. |
Enhancing Code-Switching for Cross-lingual SLU: A Unified View of Semantic and Grammatical Coherence (2023.emnlp-main)
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| Challenge: | Existing models rely on annotated training data, limiting their scalability to low-resource languages. |
| Approach: | They propose a method termed SoGo for zero-shot cross-lingual SLU that uses keywords as substitution options to extract keywords and a token-level alignment strategy to ensure grammatical coherence. |
| Outcome: | The proposed method improves zero-shot cross-lingual SLU across nine languages on MultiATIS++. |
ML-LMCL: Mutual Learning and Large-Margin Contrastive Learning for Improving ASR Robustness in Spoken Language Understanding (2023.findings-acl)
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| Challenge: | Despite efforts to improve ASR robustness, errors from pipeline approaches can lead to error propagation. |
| Approach: | They propose a framework for improving ASR robustness in SLU by using mutual learning and large-margin contrastive learning. |
| Outcome: | The proposed framework outperforms existing models and achieves new state-of-the-art performance on three datasets. |
Enhancing Dialogue State Tracking Models through LLM-backed User-Agents Simulation (2024.acl-long)
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| Challenge: | Experimental results show that the model can be used to generate dialogues in new domains quickly. |
| Approach: | They propose to use LLMs to generate dialogue data to reduce dialogue collection and annotation costs. |
| Outcome: | The proposed model performs better than the baseline model trained on real data. |
Towards Unified Spoken Language Understanding Decoding via Label-aware Compact Linguistics Representations (2023.findings-acl)
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| Challenge: | Existing methods for intent detection and slot filling decoders could result in misaligned predictions for both tasks. |
| Approach: | They propose a method that leverages label embeddings to jointly guide the decoding process. |
| Outcome: | The proposed method outperforms existing methods on two single- and multi-intent SLU benchmarks and can be incorporated into existing models. |
What are the Generator Preferences for End-to-end Task-Oriented Dialog System? (2024.emnlp-main)
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| Challenge: | Existing methods to improve the accuracy of entity retrieval are not effective. |
| Approach: | They propose a framework that improves the performance of task-oriented dialogue systems by obtaining fine-grained matching information between contexts and entities and extracting the entity attribute shift matrix as preference signals. |
| Outcome: | The proposed framework outperforms existing methods and improves the quality of the dialogue. |
RAG-HAT: A Hallucination-Aware Tuning Pipeline for LLM in Retrieval-Augmented Generation (2024.emnlp-industry)
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| Challenge: | Retrieval-augmented generation (RAG) has emerged as a significant advancement in the field of large language models (LLMs). |
| Approach: | They propose a method that uses hallucination detection labels to correct hallucines by integrating up-to-date information into their initial training. |
| Outcome: | The proposed method is based on the Retrieval Augmented Generation (RAG) method, which has shown to be effective in mitigating hallucinations and improving answer quality. |
PCAD: Towards ASR-Robust Spoken Language Understanding via Prototype Calibration and Asymmetric Decoupling (2024.acl-long)
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| Challenge: | Spoken language understanding (SLU) suffers from error propagation from automatic speech recognition (ASR) in actual scenarios. |
| Approach: | They propose a framework which calibrates bias and errors and achieves adaptive-balanced decoupling training by a prototype-based loss model. |
| Outcome: | The proposed framework outperforms existing approaches and achieves state-of-the-art performance on three datasets. |
Towards Multi-modal Sarcasm Detection via Disentangled Multi-grained Multi-modal Distilling (2024.lrec-main)
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| Challenge: | Existing approaches to sarcasm detection focus on textual and intra-modal incongruity . mainstream approaches process input of each modality in a holistic manner, resulting in redundant and unrefined information. |
| Approach: | They propose a framework for multi-modal sarcasm detection that disentangles modality representations into latent spaces and conducts multi-grained knowledge distilling. |
| Outcome: | The proposed framework overpowers existing methods on a common benchmark. |
Zero-Shot Spoken Language Understanding via Large Language Models: A Preliminary Study (2024.lrec-main)
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| Challenge: | Recent advances in large language models (LLMs) have shown promising results in zero-shot settings, which motivates us to explore prompt-based methods. |
| Approach: | They propose a two-stage framework which transforms the SLU task into a question-answering problem by directly prompting LLMs. |
| Outcome: | The proposed framework can be built by directly prompting LLMs to understand user needs without training data. |
MCLF: A Multi-grained Contrastive Learning Framework for ASR-robust Spoken Language Understanding (2023.findings-emnlp)
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| Challenge: | Trending ASR-robust SLU systems have seen impressive improvements through global contrastive learning, but they can easily lead to severe semantic changes. |
| Approach: | They propose a two-stage multi-grained contrastive learning framework to improve ASR robustness . they first adapt pre-trained language models to downstream SLU datasets and then fine-tune it on the corresponding dataset. |
| Outcome: | The proposed framework improves on four datasets and four BERT-like backbone models. |
Global Context or Local Detail? Adaptive Visual Grounding for Hallucination Mitigation (2026.findings-acl)
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Yubo Jiang, Xin Yang, Abudukelimu Wuerkaixi, Zheming Yuan, Xuxin Cheng, Cao Liu, Ke Zeng, Fengying Xie, Zhiguo Jiang, Haopeng Zhang
| Challenge: | Large vision–language models suffer from object-existence hallucinations when multi-step deliberation decouples from visual evidence. |
| Approach: | They propose a framework that allocates visual computation by uncertainty . they propose highlighting retains global context, while selective zoom-in performs local verification. |
| Outcome: | The proposed framework reduces the complexity of multimodal reasoning by minimizing the operator trade-off. |
When 20 Agents Fail to Sort: The Distributed Sorting Benchmark for Scalable Multi-Agent Systems (2026.findings-acl)
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| Challenge: | MAS-BENCH isolates coordination under explicit communication constraints . CAMOC significantly improves coordination success and efficiency across backends . |
| Approach: | They propose a distributed-sorting benchmark that isolates coordination under explicit communication constraints. |
| Outcome: | MAS-BENCH improves coordination success and efficiency across backends . CAMOC significantly improves efficiency under shared-state interaction . |
Syntax Matters: Towards Spoken Language Understanding via Syntax-Aware Attention (2023.findings-emnlp)
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| Challenge: | Existing studies on SLU systems have focused on integrating syntactic information into language models. |
| Approach: | They propose a model where attention scopes are constrained based on syntactic relationships. |
| Outcome: | The proposed model improves on three datasets and can be integrated into other language models to further boost their performance. |
MMRA: A Benchmark for Evaluating Multi-Granularity and Multi-Image Relational Association Capabilities in Large Visual Language Models (2026.findings-eacl)
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Siwei Wu, King Zhu, Yu Bai, Yiming Liang, Yizhi Li, Haoning Wu, Jiaheng Liu, Ruibo Liu, Xingwei Qu, Xuxin Cheng, Ge Zhang, Wenhao Huang, Chenghua Lin
| Challenge: | Current multimodal benchmarks focus on facts within individual images, but neglect associative relations among multiple images. |
| Approach: | They propose a multi-image relational association task and a MMRA benchmark to evaluate LVLMs. |
| Outcome: | The proposed benchmarks show that entity-level multi-image perception tasks pose greater challenges than image-level tasks. |
MoE-SLU: Towards ASR-Robust Spoken Language Understanding via Mixture-of-Experts (2024.findings-acl)
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| Challenge: | Spoken language understanding (SLU) is a crucial task in task-oriented dialogue systems. |
| Approach: | They propose an ASR-Robust SLU framework based on the mixture-of-experts technique to generate additional transcripts from clean transcripts and use it to weigh the representations of the generated transcripts, ASR transcripts . |
| Outcome: | The proposed framework achieves state-of-the-art on three benchmark SLU datasets. |
Alignment before Awareness: Towards Visual Question Localized-Answering in Robotic Surgery via Optimal Transport and Answer Semantics (2024.lrec-main)
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| Challenge: | Recent models for visual question localized-answering (VQLA) lack the ability to relate these answers to their localization at an instance level. |
| Approach: | They propose a model which introduces optimal transport to achieve bidirectional and fine-grained alignment between images and questions, enabling more precise localization. |
| Outcome: | The proposed model outperforms state-of-the-art models on two widely-used datasets on surgical scenes and surgical instruments. |
Soul-Mix: Enhancing Multimodal Machine Translation with Manifold Mixup (2024.acl-long)
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| Challenge: | Multimodal machine translation (MMT) aims to improve the performance of machine translation with the help of visual information. |
| Approach: | They propose a multimodal machine translation mixup method that integrates visual information into conventional text-only neural machine translation systems. |
| Outcome: | The proposed method outperforms existing models on a multi-directional dataset with fewer parameters and achieves new state-of-the-art performance. |