Papers by Shanshan Huang
GCML: Gradient Coherence Guided Meta-Learning for Cross-Domain Emerging Topic Rumor Detection (2025.emnlp-main)
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| Challenge: | Existing domain adaptation rumor detection methods ignore the data generalization differences and rely on a large amount of unlabeled target domain samples to achieve domain adaptation. |
| Approach: | They propose a Gradient Coherence guided Meta-Learning approach for emerging topics rumor detection that selectively learns more "generalizable" tasks that are more beneficial in adapting to the target domain. |
| Outcome: | The proposed method outperforms baselines on real-world datasets and significantly outperformed traditional methods on the in-domain condition. |
Neural Chain-of-Thought Search: Searching the Optimal Reasoning Path to Enhance Large Language Models (2026.findings-acl)
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| Challenge: | Recent research indicates that Large Reasoning Models suffer from a strategic bottleneck at reasoning path planning. |
| Approach: | They propose a framework that reformulates reasoning as a dynamic search for the optimal thinking strategy. |
| Outcome: | The proposed framework improves accuracy and computational cost while reducing generation length by over 22%. |
Gated Mechanism Enhanced Multi-Task Learning for Dialog Routing (2022.coling-1)
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| Challenge: | Existing methods for dialog routing are mostly heuristic and cannot achieve high-quality performance. |
| Approach: | They propose a multi-task learning framework with a dialog encoder and two tailored gated mechanism modules to solve this problem. |
| Outcome: | The proposed model can play the role of hierarchical information filtering and is non-invasive to existing dialog systems. |
CEM: Machine-Human Chatting Handoff via Causal-Enhance Module (2022.emnlp-main)
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| Challenge: | Existing methods to predict chatbot failure ignore causal variables, resulting in cost increasement and prediction bias. |
| Approach: | They propose a machine-human chatting handoff module that predicts chatbot failure . they use user state and labor cost to correct the prediction bias . |
| Outcome: | The proposed method improves the performance of existing methods without any elaborate model crafting. |
AssoCiAm: A Benchmark for Evaluating Association Thinking while Circumventing Ambiguity (2025.emnlp-main)
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| Challenge: | Recent advances in multimodal large language models (MLLMs) have garnered significant attention, offering a promising pathway toward artificial general intelligence (AGI). |
| Approach: | They propose a benchmark to evaluate associative ability while circumventing the inherent ambiguity in association tasks by decomposing ambiguities into two types and propose 'assoCiAm' they conduct extensive experiments on MLLMs, revealing a strong positive correlation between cognition and association. |
| Outcome: | The proposed method shows that ambiguity in association evaluations makes MLLMs more random-like and the model's behavior more random. |
MarkQA: A large scale KBQA dataset with numerical reasoning (2023.emnlp-main)
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| Challenge: | Existing KBQA datasets are insufficient for numerical reasoning . existing KBqa datasets lack multi-hop reasoning and numerical reasoning. |
| Approach: | They propose a task that necessitates the ability to perform multi-hop reasoning and numerical reasoning. |
| Outcome: | The proposed task necessitates the ability to perform multi-hop reasoning and numerical reasoning. |
MultiFinBen: Benchmarking Large Language Models for Multilingual and Multimodal Financial Application (2026.acl-long)
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Xueqing Peng, Lingfei Qian, Yan Wang, Ruoyu Xiang, Yueru He, Yang Ren, Mingyang Jiang, Vincent Jim Zhang, Yuqing Guo, Jeff Zhao, Huan He, Yi Han, Yun Feng, Yuechen Jiang, Yupeng Cao, Haohang Li, Yangyang Yu, Xiaoyu Wang, Penglei Gao, Shengyuan Lin, Keyi Wang, Shanshan Yang, Yilun Zhao, Zhiwei Liu, Peng Lu, Jerry Huang, Suyuchen Wang, Triantafillos Papadopoulos, Polydoros Giannouris, Efstathia Soufleri, Nuo Chen, Zhiyang Deng, Heming Fu, Yijia Zhao, Mingquan Lin, Meikang Qiu, Kaleb E Smith, Arman Cohan, Xiao-Yang Liu, Jimin Huang, Guojun Xiong, Alejandro Lopez-Lira, Xi Chen, Junichi Tsujii, Jian-Yun Nie, Sophia Ananiadou, Qianqian Xie
| Challenge: | Existing evaluations of LLMs in finance are text-only, monolingual, and largely saturated by current models. |
| Approach: | They propose a multilingual and multimodal benchmark for evaluating LLMs in real financial contexts. |
| Outcome: | The first expert-annotated multilingual and multimodal benchmark is released . it evaluates 21 leading LLMs and shows they perform better in multilingual settings . |
MONTROSE: LLM-driven Monte Carlo Tree Search Self-Refinement for Cross-Domain Rumor Detection (2025.findings-acl)
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| Challenge: | Existing feature alignment methods are susceptible to task interference during training. |
| Approach: | MONTROSE is a cross-domain rumor detection method that generates high-quality synthetic data for the target domain and a domain-sharpness-aware approach to train models with these synthetic data. |
| Outcome: | Experiments show that MONTROSE improves in cross-domain rumor detection. |
ExtRA: Extracting Prominent Review Aspects from Customer Feedback (D18-1)
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| Challenge: | Existing methods for analyzing and summarizing customer reviews are based on a number of prominent review aspects. |
| Approach: | They propose a framework for extracting the most prominent aspects of a given product type from textual reviews. |
| Outcome: | The proposed framework extracts K most prominent aspect terms which do not overlap semantically without supervision. |
GATE: Graph-based Adaptive Tool Evolution Across Diverse Tasks (2026.acl-long)
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Jianwen Luo, Yiming Huang, Jinxiang Meng, Fangyu Lei, Shizhu He, Xiao Liu, Shanshan Jiang, Bin Dong, Jun Zhao, Kang Liu
| Challenge: | Existing toolsets that use large language models are limited to single-task settings. |
| Approach: | They propose a framework that dynamically constructs and evolves a hierarchical graph of reusable tools across multiple scenarios. |
| Outcome: | The proposed framework achieves up to 4.3 faster milestone completion in Minecraft compared to the previous state-of-the-art method and provides an average improvement of 9.23% over existing tool-making methods in code generation tasks and 10.03% in agent tasks. |
RouterEval: A Comprehensive Benchmark for Routing LLMs to Explore Model-level Scaling Up in LLMs (2025.findings-emnlp)
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| Challenge: | a lack of comprehensive benchmarks for Routing large language models has hindered the development of routers. |
| Approach: | They propose a router-based benchmark to evaluate Routing large language models . the benchmark includes performance records for 12 popular LLM evaluations . |
| Outcome: | The proposed model-level scaling up phenomenon can surpass the best single model in the pool and many existing strong LLMs. |
TARGA: Targeted Synthetic Data Generation for Practical Reasoning over Structured Data (2025.acl-long)
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| Challenge: | Existing methods for semantic parsing rely on extensive manually annotated datasets and limited generalization capability to unseen examples. |
| Approach: | They propose a framework that generates high-relevance synthetic data without manual annotation . they generate queries for the queries and use them as demonstrations for in-context learning . |
| Outcome: | The proposed framework outperforms non-fine-tuned methods on KBQA datasets and shows superior sample efficiency, robustness, and generalization capabilities under non-I.I.D. settings. |
Statistically Profiling Biases in Natural Language Reasoning Datasets and Models (2023.findings-emnlp)
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| Challenge: | Existing methods to evaluate NLP models' weaknesses are limited by “hypothesis-only” tests and CheckLists. |
| Approach: | They propose a lightweight general statistical profiling framework that automatically identifies potential biases in multiple-choice NLU datasets without requiring additional test cases. |
| Outcome: | The proposed framework assesses the extent to which models exploit these biases through black-box testing, confirming prior findings and revealing new insights. |
MiniLongBench: The Low-cost Long Context Understanding Benchmark for Large Language Models (2025.acl-long)
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| Challenge: | Existing LCU benchmarks for large language models often result in prohibitively high evaluation costs . existing benchmarks exhibit significant redundancy, which means inefficiency in evaluation . |
| Approach: | They propose a data compression method tailored for long-text data with sparse information characteristics. |
| Outcome: | The proposed method reduces evaluation costs to 4.5% of the long-text benchmark LongBench . the proposed method is based on a long-term LCU benchmark with sparse information characteristics . |
QueryAgent: A Reliable and Efficient Reasoning Framework with Environmental Feedback based Self-Correction (2024.acl-long)
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| Challenge: | Existing methods for semantic parsing fail when hallucinations are encountered . QueryAgent solves a question step-by-step and performs stepwise self-correction . |
| Approach: | They propose a framework that solves a query step-by-step and performs stepwise self-correction. |
| Outcome: | The proposed framework outperforms existing methods on GrailQA and GraphQ by 5.7 and 15.0 points. |
MoExtend: Tuning New Experts for Modality and Task Extension (2024.acl-srw)
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| Challenge: | Existing instruction tuning methods for large language models (LLMs) are costly and difficult to implement. |
| Approach: | They propose a framework to streamline the modality adaptation and extension of Mixture-of-Experts (MoE) models. |
| Outcome: | The proposed framework enables rapid adaptation and extension to new modal data or tasks without tuning pretrained models. |