Papers by Yutai Hou
Learning to Bridge Metric Spaces: Few-shot Joint Learning of Intent Detection and Slot Filling (2021.findings-acl)
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| Challenge: | Existing few-shot learning methods learn a single task each time with a few examples . but, real-world applications often contain multiple closely related tasks . |
| Approach: | They propose a few-shot joint learning scheme that captures intent and slot relationships from only a handful of examples and adapts the bridged metric space to specific few- shot domain. |
| Outcome: | The proposed model outperforms baseline models on two public datasets on intent and slot . the proposed model significantly outperformed baseline models in one and five shots settings. |
Tool Zero: Training Tool-Augmented LLMs via Pure RL from Scratch (2025.findings-emnlp)
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Yirong Zeng, Xiao Ding, Yutai Hou, Yuxian Wang, Li Du, Juyi Dai, Qiuyang Ding, Duyu Tang, Dandan Tu, Weiwen Liu, Bing Qin, Ting Liu
| Challenge: | Experimental results demonstrate that our models achieve over 7% performance improvement compared to both SFT and RL-with-SFT models under the same experimental settings. |
| Approach: | They propose a dynamic generalization-guided reward design for rule-based RL that shifts rewards from exploratory to exploitative tool-use patterns. |
| Outcome: | The proposed model achieves over 7% performance improvement compared to SFT and RL-with-SFT models under the same experimental settings. |
MetaPrompting: Learning to Learn Better Prompts (2022.coling-1)
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| Challenge: | Recent research on prompting moves from discrete tokens based "hard prompts" to continuous "soft prompts", which employ learnable vectors as pseudo prompt tokens and achieve better performance. |
| Approach: | They propose a generalized soft prompting method that uses model-agnostic meta-learning to find better initialization for soft prompts. |
| Outcome: | The proposed method improves on three datasets and brings new state-of-the-art performance. |
Sequence-to-Sequence Data Augmentation for Dialogue Language Understanding (C18-1)
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| Challenge: | Existing work which augments an utterance without considering its relation with other utterrances, however, has failed to improve the language understanding module. |
| Approach: | They propose a sequence-to-sequence generation based data augmentation framework that leverages one utterance’s same semantic alternatives in the training data. |
| Outcome: | The proposed framework achieves 6.38 and 10.04 F-scores on the Airline Travel Information System dataset and a newly created semantic frame annotation on the Stanford Multi-turn, Multi-domain Dialogue Dataset. |
Beyond Static Evaluation: A Dynamic Approach to Assessing AI Assistants’ API Invocation Capabilities (2024.lrec-main)
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| Challenge: | Existing evaluation methods for human-machine interactions are static and can be misleading. |
| Approach: | They propose to use a LLM-based user agent to assess an assistant's API call capability without human involvement. |
| Outcome: | The proposed method mirrors real human conversation patterns in human-machine interactions, and shows that it aligns more closely with human assessment. |
Concise and Precise Context Compression for Tool-Using Language Models (2024.findings-acl)
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Yang Xu, Yunlong Feng, Honglin Mu, Yutai Hou, Yitong Li, Xinghao Wang, Wanjun Zhong, Zhongyang Li, Dandan Tu, Qingfu Zhu, Min Zhang, Wanxiang Che
| Challenge: | Existing methods suffer from key information loss and difficulty in adjusting the length of compressed sequences based on documentation lengths. |
| Approach: | They propose two strategies for compressing tool documentation into concise and precise summary sequences for tool-using language models. |
| Outcome: | The proposed approach achieves comparable performance to the upper-bound baseline under 16x compression ratio. |
MDC-Bench: A Multidisciplinary Causal Benchmark Based on Causal Structures for Evaluating Large Language Models (2026.findings-acl)
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Peng Wang, Yuxiong Yan, Xiao Ding, Kai Xiong, Bibo Cai, Chao Peng, Yutai Hou, Dandan Tu, Bing Qin, Ting Liu
| Challenge: | Existing causal datasets focus on the commonsense domain, but LLMs perform poorly when answering complex questions. |
| Approach: | They propose a multidisciplinary causal evaluation benchmark to assess LLMs' knowledge and skills. |
| Outcome: | The proposed model improves in domain specialization, structural diversity, and task complexity. |
Recall and Learn: Fine-tuning Deep Pretrained Language Models with Less Forgetting (2020.emnlp-main)
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| Challenge: | Existing methods to fine-tune deep pretrained language models face catastrophic forgetting problems. |
| Approach: | They propose a recall and learn mechanism which integrates pretraining and downstream tasks into a single mechanism. |
| Outcome: | The proposed method achieves state-of-the-art performance on the GLUE benchmark and better average performance than directly fine-tuning of BERT-large. |
Language Anisotropic Cross-Lingual Model Editing (2023.findings-acl)
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| Challenge: | Existing work studies monolingual model editing, which lacks cross-lingual transferability to perform editing simultaneously across languages. |
| Approach: | They propose a framework to naturally adapt monolingual model editing approaches to the cross-lingual scenario using parallel corpus. |
| Outcome: | The proposed framework adapts monolingual model editing approaches to the cross-lingual scenario using parallel corpus and amplifies different subsets of parameters for each language. |
Few-shot Slot Tagging with Collapsed Dependency Transfer and Label-enhanced Task-adaptive Projection Network (2020.acl-main)
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| Challenge: | Existing few-shot learning methods for slot tagging are based on similarity-based methods, but they are difficult to apply to an unseen domain due to the discrepancy of label sets. |
| Approach: | They propose a label-enhanced task-adaptive projection network to transfer abstract label dependency patterns as transition scores into the conditional random field (CRF) Experimental results show that their model significantly outperforms the strongest few-shot learning baseline by 14.64 F1 scores in the one-shot setting. |
| Outcome: | The proposed model outperforms the strongest few-shot learning baseline by 14.64 F1 scores in the one-shot setting. |
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. |
iTool: Reinforced Fine-Tuning with Dynamic Deficiency Calibration for Advanced Tool Use (2025.emnlp-main)
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Yirong Zeng, Xiao Ding, Yuxian Wang, Weiwen Liu, Yutai Hou, Wu Ning, Xu Huang, Duyu Tang, Dandan Tu, Bing Qin, Ting Liu
| Challenge: | Synthesizing tool-use data through real-world simulations is effective for enhancing large language models (LLMs) however, training gains decay as synthetic data increases, and the model struggles to benefit from more synthetic data. |
| Approach: | They propose an iterative reinforced fine-tuning strategy to improve LLMs with external tools to augment their capabilities. |
| Outcome: | The proposed method achieves 13.11% better performance than the same-size base model and outperforms larger open-source and closed-source models. |
Inverse is Better! Fast and Accurate Prompt for Few-shot Slot Tagging (2022.findings-acl)
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| Challenge: | Recent results show that prompting methods are inefficient for slot tagging tasks . inverse prompting only requires a one-turn prediction for each slot type . |
| Approach: | They propose an inverse prompting paradigm that reversely predicts slot values given slot types . the method is faster and significantly improves the effect on 10-shot setting . |
| Outcome: | The proposed method improves over 6.1 F1-scores on 10-shot setting and achieves new state-of-the-art performance. |
TinyJudge: Unverifiable Constraint Alignment via Lightweight Specialist Ensembles (2026.acl-long)
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Yirong Zeng, Yufei Liu, Xiao Ding, Yutai Hou, Yuxian Wang, Wu Ning, Haonan Song, Dandan Tu, Qixun Zhang, Yuxiang He, Bibo Cai, Ting Liu
| Challenge: | Instruction Following (IF) is a core capability of LLMs, requiring strict adherence to diverse constraints. |
| Approach: | They propose a framework that uses tiny language models to evaluate instruction following . they propose to use a set of specialized tiny language model to provide rewards for soft constraints. |
| Outcome: | The proposed framework outperforms baseline models by 12% and speeds up training time by 3. |