Papers by Shijue Huang
CGIM: A Cycle Guided Interactive Learning Model for Consistency Identification in Task-oriented Dialogue (2022.coling-1)
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| Challenge: | Consistency identification in task-oriented dialog usually consists of three subtasks . a proposed model for consistency identification in dialog is based on an explicit interaction paradigm . |
| Approach: | They propose a cycle guided interactive learning model that makes information exchange explicit from all the three tasks. |
| Outcome: | The proposed model achieves state-of-the-art performance pushing the overall score to 56.3% (5.0% point absolute improvement) |
Self-Reasoning Language Models: Unfold Hidden Reasoning Chains with Few Reasoning Catalyst (2025.findings-acl)
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| Challenge: | Recent studies have demonstrated that inference-time scaling increases performance of Large Language Models (LLMs) in various reasoning tasks such as mathematics and complex question answering by increasing the length of Chain-of-Thought (CoT). |
| Approach: | They propose a model which synthesizes longer CoT data and iteratively improves performance through self-training by incorporating a few demonstration examples. |
| Outcome: | The proposed model achieves an average improvement of more than +2.5 points across five reasoning tasks: MMLU, GSM8K, ARC-C, HellaSwag, and BBH on two backbone models. |
Cross-lingual Prompting: Improving Zero-shot Chain-of-Thought Reasoning across Languages (2023.emnlp-main)
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| Challenge: | Existing methods for zero-shot CoT are limited to a single language, making it difficult to generalize to other languages and hindering global development. |
| Approach: | They introduce cross-lingual prompting (CLP) to improve zero-shot CoT reasoning across languages. |
| Outcome: | The proposed method outperforms existing prompting methods on several benchmarks. |
MMSD2.0: Towards a Reliable Multi-modal Sarcasm Detection System (2023.findings-acl)
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| Challenge: | Existing benchmarks for multi-modal sarcasm detection have some shortcomings . a new framework can leverage multi-grained cues from multiple perspectives for multimodal detection . |
| Approach: | They propose a correction dataset that removes spurious cues and re-annotates the unreasonable samples. |
| Outcome: | The proposed framework outperforms the existing benchmarks in multi-modal sarcasm detection. |
CLongEval: A Chinese Benchmark for Evaluating Long-Context Large Language Models (2024.findings-emnlp)
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| Challenge: | Developing long-context LLMs with robust long-text capabilities is underdeveloped due to a lack of benchmarks. |
| Approach: | They propose a Chinese benchmark for evaluating long-context LLMs with Chinese capabilities. |
| Outcome: | The proposed model is based on 6 open-source LLMs and 2 commercial ones. |
CostBench: Evaluating Multi-Turn Cost-Optimal Planning and Adaptation in Dynamic Environments for LLM Tool-Use Agents (2026.acl-long)
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| Challenge: | Existing evaluations of Large Language Models (LLMs) focus on task completion, but neglect a crucial capability: the ability to devise and adjust cost-optimal plans in response to changing environments. |
| Approach: | They propose a scalable, cost-centric benchmark to evaluate agents’ economic reasoning and replanning abilities. |
| Outcome: | Evaluating leading open-sourced and proprietary models on CostBench reveals a substantial gap in cost-aware planning . |
Don’t be Contradicted with Anything! CI-ToD: Towards Benchmarking Consistency for Task-oriented Dialogue System (2021.emnlp-main)
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| Challenge: | Consistency Identification has been used for preventing inconsistent response generation, but few efforts have been made to task-oriented dialogue. |
| Approach: | They propose a dataset for Consistency Identification in task-oriented dialog system. |
| Outcome: | The proposed dataset is based on a single label and provides fine-grained labels to encourage model to know what inconsistent sources lead to it. |
Self-Sum: Teaching an Agent to Decide Itself When and What to Summarize (2026.findings-acl)
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Hongru Wang, Rui Wang, Jushi Kai, Boyang Xue, Yongqi Li, Shijue Huang, Xiaoteng Ma, Jeff Z. Pan, Amos Storkey
| Challenge: | Existing methods for summarizing long-horizon agents rely on fixed, rule-based summarization strategies. |
| Approach: | They propose a framework that empowers agents to autonomously decide when and what to summarize by modeling it as an internal cognitive action unified with environmental actions. |
| Outcome: | The proposed framework outperforms no-summarization and rule-based training methods on long-horizon benchmarks and shows strong generalization gains. |
Planning, Creation, Usage: Benchmarking LLMs for Comprehensive Tool Utilization in Real-World Complex Scenarios (2024.findings-acl)
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Shijue Huang, Wanjun Zhong, Jianqiao Lu, Qi Zhu, Jiahui Gao, Weiwen Liu, Yutai Hou, Xingshan Zeng, Yasheng Wang, Lifeng Shang, Xin Jiang, Ruifeng Xu, Qun Liu
| Challenge: | Existing benchmarks focus on simple synthesized queries that do not reflect real-world complexity, thereby offering limited perspectives in evaluating tool utilization. |
| Approach: | They propose a benchmark to evaluate LLMs’ ability in tool utilization within real-world scenarios. |
| Outcome: | The proposed benchmark improves LLMs’ ability in tool utilization within real-world scenarios and eliminates the restriction of pre-defined toolset. |