Papers by YongKang Liu
Why Do More Experts Fail? A Theoretical Analysis of Model Merging (2026.acl-long)
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Zijing Wang, Xingle Xu, YongKang Liu, Yiqun Zhang, Peiqin Lin, Shi Feng, Daling Wang, Xiaocui Yang, Hinrich Schuetze
| Challenge: | Existing methods for model merging struggle to maintain performance gains as the number of merged models increases. |
| Approach: | They propose a Reparameterized Heavy-Tailed method to extend the merged model’s coverage and enhance performance. |
| Outcome: | The proposed method extends the merged model’s coverage and enhances performance on 19 benchmarks, including knowledge-intensive and general-purpose tasks. |
MoLAN: A Unified Modality-Aware Noise Dynamic Editing Framework for Multimodal Sentiment Analysis (2026.findings-acl)
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| Challenge: | Existing approaches to multimodal sentiment analysis treat entire modality as an independent unit for feature enhancement or denoising, which often suppresses redundant noise at the cost of weakening critical information. |
| Approach: | They propose a ModaLity-aware noise dynAmic editiNg framework that performs modality-awful block partitioning by dividing features of each modality into multiple blocks. |
| Outcome: | Experiments on five models and four datasets show that MoLAN+ achieves the state-of-the-art performance. |
HiFT: A Hierarchical Full Parameter Fine-Tuning Strategy (2024.emnlp-main)
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| Challenge: | Existing approaches to fine-tuning language models use zeroth-order optimizers to conserve GPU memory. |
| Approach: | They propose a full-parameter fine-tuning strategy which updates a subset of parameters at each training step. |
| Outcome: | The proposed approach reduces the amount of gradients and optimizer state parameters residing in GPU memory at the same time, thereby reducing GPU memory usage. |
MUSE: A Multimodal Conversational Recommendation Dataset with Scenario-Grounded User Profiles (2025.findings-acl)
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| Challenge: | Existing research focuses solely on text, leaving a gap with practical applications. |
| Approach: | They propose to synthesize a multimodal conversational recommendation dataset using multimodal large language models to automatically synthesized data from 7,000 conversations in the Clothing domain. |
| Outcome: | The proposed dataset contains 83,148 utterances from 7,000 conversations centered around the Clothing domain. |
SAD: A Large-Scale Strategic Argumentative Dialogue Dataset (2026.acl-long)
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YongKang Liu, Jiayang Yu, Mingyang Wang, Yiqun Zhang, Ercong Nie, Shi Feng, Daling Wang, Kaisong Song, Hinrich Schuetze
| Challenge: | Argumentation is a key part of human reasoning and decision-making . existing argumentative corpora focus on single-turn settings, but multi-turn dialogues are often realized as multi-turned dialogues . |
| Approach: | They present a dataset for strategic multi-turn argumentation dialogues . they annotate each utterance with five strategy types, allowing multiple strategies per utterrance . |
| Outcome: | The proposed dataset shows that explicit prompting improves fluency, stylistic coherence and persuasiveness. |
Pixel-Level Reasoning Segmentation via Multi-turn Conversations (2025.acl-long)
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| Challenge: | Existing visual perception systems focus on region-level segmentation in single-turn dialogues . existing systems cannot reason at the pixel level and comprehend dynamic user intent . |
| Approach: | They propose a task that tracks evolving user intent via multi-turn interactions for fine-grained segmentation. |
| Outcome: | The proposed method outperforms existing baselines in segmentation and reasoning metrics. |
SSMLoRA: Enhancing Low-Rank Adaptation with State Space Model (2025.naacl-long)
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| Challenge: | Fine-tuning requires substantial computational resources and is prone to overfitting when applied to small datasets. |
| Approach: | They propose a parameter-efficient fine-tuning method that integrates a State Space Model (SSM) to interconnect low-rank matrices. |
| Outcome: | The proposed method achieves comparable performance to LoRA on the general language understanding evaluation (GLUE) benchmark while using only half the parameters. |
Look Within or Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning (2026.acl-long)
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| Challenge: | Parameter-Efficient Fine-Tuning (PEFT) is an alternative to Full-Parameter Fine-tuning, but its effectiveness on complex tasks such as reasoning and instruction-following remains unclear. |
| Approach: | They propose to use PEFT to reduce the number of trainable parameters while freezing the weights of LLMs. |
| Outcome: | The proposed methods perform well on standard tasks, but weaknesses on complex and adversarial settings call for new directions beyond current paradigms. |
PlaM: Training-Free Plateau-Guided Model Merging for Better Visual Grounding in MLLMs (2026.findings-acl)
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Zijing Wang, YongKang Liu, Mingyang Wang, Ercong Nie, Deyuan Chen, Zhengjie Zhao, Shi Feng, Daling Wang, Xiaocui Yang, Yifei Zhang, Hinrich Schuetze
| Challenge: | Multimodal instruction fine-tuning degrades textual reasoning capability, undermining multimodal performance. |
| Approach: | They propose a plateau-guided model merging method that selectively injects base language model parameters into MLLMs to mitigate this degradation. |
| Outcome: | The proposed framework reduces multimodal instruction fine-tuning degradation by incorporating a plateau-guided model merging method into MLLMs. |
NEAT: Neuron-Based Early Exit for Large Reasoning Models (2026.findings-acl)
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| Challenge: | Existing approaches to reduce overthinking require additional rollout computation or externally labeled datasets. |
| Approach: | They propose a Neuron-based Early reAsoning exiT framework that monitors neuron-level activation dynamics to enable training-free early exits. |
| Outcome: | The proposed framework reduces the amount of reasoning steps generated by LRMs while maintaining accuracy. |
SolEval: Benchmarking Large Language Models for Repository-level Solidity Smart Contract Generation (2025.emnlp-main)
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| Challenge: | Existing methods focus on Python and Java, neglecting Solidity, the programming language for Ethereum smart contracts. |
| Approach: | They construct a repository-level benchmark for Solidity to evaluate the performance of LLMs on Ethereum. |
| Outcome: | The proposed benchmarks show that the best performing LLM achieves only 26.29% Pass@10, highlighting room for improvement in Solidity code generation. |
SAFE-QAQ: End-to-End Slow-Thinking Audio-Text Fraud Detection via Reinforcement Learning (2026.acl-long)
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Peidong Wang, Zhiming Ma, Xin Dai, YongKang Liu, Shi Feng, Xiaocui Yang, Wenxing Hu, Zhihao Wang, Mingjun Pan, Li Yuan, Daling Wang
| Challenge: | Existing methods for fraud detection rely on transcribed text, lacking acoustic cues . a proposed framework for audio-based slow-thinking fraud detection eliminates transcription errors . |
| Approach: | They propose a framework for audio-based slow-thinking fraud detection that eliminates transcription errors and rewards slow-thought reasoning by capturing fine-grained audio details. |
| Outcome: | The proposed method improves accuracy, inference efficiency, and real-time processing capabilities. |