Papers by Shuo Shang
TTPA: Token-level Tool-use Preference Alignment Training Framework with Fine-grained Evaluation (2025.findings-emnlp)
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| Challenge: | Existing tool-learning methods often overlook fine-grained optimization of internal tool call details. |
| Approach: | They propose a training paradigm for constructing token-level tool-use preference datasets . reversed dataset construction is a method for creating high-quality, multi-turn tool-user datasets by reversing the generation flow. |
| Outcome: | a new training paradigm improves tool-using performance and generalizes results. |
“In-Dialogues We Learn”: Towards Personalized Dialogue Without Pre-defined Profiles through In-Dialogue Learning (2024.emnlp-main)
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| Challenge: | Existing approaches to personalized dialogue generate pre-defined profiles that are time-consuming and labor-intensive to create. |
| Approach: | They propose a framework that leverages dialogue history to characterize personas without pre-defined profiles. |
| Outcome: | The proposed framework improves BLEU and ROUGE scores on three datasets and human evaluations further validate the proposed method. |
If an LLM Were a Character, Would It Know Its Own Story? Evaluating Lifelong Learning in LLMs (2026.acl-long)
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Siqi Fan, Xiusheng Huang, Yiqun Yao, Xuezhi Fang, Kang Liu, Peng Han, Shuo Shang, Aixin Sun, Yequan Wang
| Challenge: | Existing benchmarks for large language models (LLMs) fail to capture these dynamics, focusing on static, open-ended evaluations. |
| Approach: | They propose a benchmark to assess lifelong learning in large language models . they use two episodic datasets rich in narrative structure and character interactions . |
| Outcome: | Experiments on LLMs show that non-parametric methods outperform parametric ones in managing stateful learning. |
CESRec: Constructing Pseudo Interactions for Sequential Recommendation via Conversational Feedback (2025.findings-emnlp)
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| Challenge: | Existing Sequential Recommendation Systems (SRS) rely on collaborative filtering signals and fail to capture real-time user preferences. |
| Approach: | They propose a framework that integrates the long-term preference modeling of SRS with the real-time preference elicitation of CRS. |
| Outcome: | The proposed framework integrates the long-term preference modeling of SRS with the real-time preference elicitation of CRS. |
Mobile-Bench: An Evaluation Benchmark for LLM-based Mobile Agents (2024.acl-long)
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Shihan Deng, Weikai Xu, Hongda Sun, Wei Liu, Tao Tan, Liujianfeng Liujianfeng, Ang Li, Jian Luan, Bin Wang, Rui Yan, Shuo Shang
| Challenge: | Existing benchmarks for LLM-based mobile agents are insufficient to evaluate their capabilities. |
| Approach: | They propose a benchmark to evaluate LLM-based mobile agents' planning capabilities . they expand UI operations by incorporating 103 APIs to accelerate task completion . |
| Outcome: | The proposed benchmarks are based on 103 collected APIs and real user queries . the data is categorized into three distinct groups: SAST, SAMT, and MAMT . |
PACE: Prefix-Protected and Difficulty-Aware Compression for Efficient Reasoning (2026.findings-acl)
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Ruixiang Feng, Yuntao Wen, Silin Zhou, Ke Shi, Yifan Wang, Ran Le, Zhenwei An, Zongchao Chen, Chen Yang, Guangyue Peng, Yiming Jia, Dongsheng Wang, Tao Zhang, Lisi Chen, Yang Song, Shen Gao, Shuo Shang
| Challenge: | Existing LRMs often suffer from "overthinking" and excessively long reasoning traces . a dual-level framework for length compression of LRM is proposed . |
| Approach: | They propose a framework for prefix-protected and difficulty-aware compression under hierarchical supervision. |
| Outcome: | The proposed framework reduces token usage while improving accuracy on math benchmarks. |
CharacterEval: A Chinese Benchmark for Role-Playing Conversational Agent Evaluation (2024.acl-long)
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| Challenge: | CharacterEval is a benchmark for comprehensive RPCA assessment in Chinese . authors show that Chinese LLMs exhibit more promising capabilities than GPT-4 in role-playing conversation. |
| Approach: | They propose a Chinese benchmark for comprehensive RPCA assessment . they use a dataset of Chinese role-playing dialogues and character profiles . |
| Outcome: | The proposed benchmark demonstrates that Chinese LLMs exhibit more promising capabilities than GPT-4 in Chinese role-playing conversation. |
CulFiT: A Fine-grained Cultural-aware LLM Training Paradigm via Multilingual Critique Data Synthesis (2025.acl-long)
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| Challenge: | Large Language Models exhibit a specific cultural bias, neglecting values and differences of low-resource regions. |
| Approach: | They propose a culturally-aware training paradigm that leverages multilingual data and fine-grained reward modeling to enhance cultural sensitivity and inclusivity. |
| Outcome: | The proposed model achieves state-of-the-art in cultural alignment and general reasoning. |
Position-Aware Depth Decay Decoding (D3): Boosting Large Language Model Inference Efficiency (2025.findings-acl)
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| Challenge: | Recent dynamic computation methods show that not all components are required for inference, enabling a training-free pipeline. |
| Approach: | They propose a token-position aware layer skipping framework to save 1.5x times operations efficiently while maintaining performance. |
| Outcome: | The proposed algorithm achieves 1.5x speedup on large language models with no retraining and with comparable performance on the GSM8K and BBH benchmarks. |
DetermLR: Augmenting LLM-based Logical Reasoning from Indeterminacy to Determinacy (2024.acl-long)
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| Challenge: | Recent advances in large language models (LLMs) have revolutionized the landscape of reasoning tasks. |
| Approach: | They propose a new approach that rethinks the reasoning process as an evolution from indeterminacy to determinacy. |
| Outcome: | The proposed model surpasses all baselines on various logical reasoning benchmarks. |
MobileVLM: A Vision-Language Model for Better Intra- and Inter-UI Understanding (2024.findings-emnlp)
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Qinzhuo Wu, Weikai Xu, Wei Liu, Tao Tan, Liujian Liujianfeng, Ang Li, Jian Luan, Bin Wang, Shuo Shang
| Challenge: | Recent mobile AI agents based on VLMs lack basic mobile capabilities due to their pre-trained nature. |
| Approach: | They propose a mobile AI agent based on VLMs that includes additional pre-training stages to enhance both intra- and inter-UI understanding. |
| Outcome: | The proposed model outperforms existing VLMs on the Chinese mobile dataset Mobile3M . |
Thinking Before Running! Efficient Code Generation with Thorough Exploration and Optimal Refinement (2025.findings-acl)
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| Challenge: | Recent research indicates that large language models (LLMs) have demonstrated remark-able capabilities in various programming-related domains, such as code generation and code refinement. |
| Approach: | They propose a framework that combines exploration with refinement to reduce test-time computation overhead. |
| Outcome: | The proposed framework outperforms SOTA and AgentCoder on humanEval and MBPP benchmarks while reducing test-time computation overhead and scalability. |
DPEPO: Diverse Parallel Exploration Policy Optimization for LLM-based Agents (2026.acl-long)
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JunShuo Zhang, Chengrui Huang, Feng Guo, Zihan Li, Ke Shi, Menghua Jiang, Jiguo Yu, Shuo Shang, Shen Gao
| Challenge: | Existing approaches to large language model (LLM) agents that follow the sequential "reason-then-act" paradigm suffer from limited exploration and incomplete environmental understanding as they interact with only a single environment per step. |
| Approach: | They propose a paradigm that enables an agent to interact with multiple environments simultaneously and share cross-trajectory experiences. |
| Outcome: | The proposed paradigm achieves state-of-the-art (SOTA) success rates while maintaining comparable efficiency to strong sequential baselines. |
Lock on Target! Precision Unlearning via Directional Control (2025.findings-emnlp)
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| Challenge: | Existing methods for unlearning harmful, sensitive, or outdated knowledge suffer from two critical limitations: (1) collateral forgetting, where erasing target data inadvertently removes related but desirable knowledge, and (2) generality forgetting degrades the model’s general capabilities. |
| Approach: | They propose a method that identifies and leverages a targeted "unlearning direction" in the model's parameter space and selectively updates along this direction. |
| Outcome: | Experiments show that the proposed method achieves state-of-the-art unlearning precision while preserving both related knowledge and general capabilities. |
More is not always better? Enhancing Many-Shot In-Context Learning with Differentiated and Reweighting Objectives (2025.acl-long)
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Xiaoqing Zhang, Ang Lv, Yuhan Liu, Flood Sung, Wei Liu, Jian Luan, Shuo Shang, Xiuying Chen, Rui Yan
| Challenge: | Large language models excel at few-shot in-context learning but performance plateaus as ICL demonstrations increase from a few to many. |
| Approach: | They propose a novel optimization method that optimizes the negative log-likelihood objective and reweights the model to achieve many-shot performance. |
| Outcome: | The proposed method achieves significant performance improvements across a large-scale dataset. |
GSQ-Tuning: Group-Shared Exponents Integer in Fully Quantized Training for LLMs On-Device Fine-tuning (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) fine-tuning techniques require large Floating Point(FP) computation and are impractical for resource-constrained edge devices. |
| Approach: | They propose a framework for on-device LLM fine-tuning that eliminates the need for floating-point operations in both inference and training. |
| Outcome: | The proposed framework reduces memory and compute costs while reducing memory usage. |
360∘REA: Towards A Reusable Experience Accumulation with 360∘ Assessment for Multi-Agent System (2024.findings-acl)
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| Challenge: | Recent studies focus on optimizing the agent team or employing self-reflection to iteratively solve complex tasks. |
| Approach: | They propose a hierarchical multi-agent framework that uses 360 assessment to accumulate experience through fine-grained assessment. |
| Outcome: | The proposed framework is based on corporate organizational practices and employs a dual-level experience pool for agents to accumulate experience through fine-grained assessment. |
DNASpeech: A Contextualized and Situated Text-to-Speech Dataset with Dialogues, Narratives and Actions (2025.acl-long)
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| Challenge: | Existing TTS datasets lack situated descriptive prompts aligned with speech data. |
| Approach: | They propose a contextualized and situated text-to-speech task to promote more accurate and customized speech generation using DNA prompts. |
| Outcome: | The proposed task promotes more accurate and customized speech generation using DNA prompts. |