Papers by Haoming Jiang
Graph Reasoning for Question Answering with Triplet Retrieval (2023.findings-acl)
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| Challenge: | Existing methods to answer complex questions require reasoning over knowledge graphs (KGs) state-of-the-art methods constrain retrieved knowledge in local subgraphs and discard more diverse triplets that are disconnected but useful for question answering. |
| Approach: | They propose a method to retrieve the most relevant triplets from KGs and then rerank them, which are then concatenated with questions to be fed into language models. |
| Outcome: | The proposed method outperforms state-of-the-art methods on commonsenseQA and OpenbookQA datasets with 4.6% absolute accuracy. |
BlendFilter: Advancing Retrieval-Augmented Large Language Models via Query Generation Blending and Knowledge Filtering (2024.emnlp-main)
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Haoyu Wang, Ruirui Li, Haoming Jiang, Jinjin Tian, Zhengyang Wang, Chen Luo, Xianfeng Tang, Monica Cheng, Tuo Zhao, Jing Gao
| Challenge: | Retrieval-augmented Large Language Models struggle with complex inputs and noisy knowledge retrieval hindering model effectiveness. |
| Approach: | They propose a query generation method that integrates query generation blending with knowledge filtering to enhance retrieval-augmented LLMs. |
| Outcome: | The proposed approach surpasses state-of-the-art benchmarks on open-domain question answering benchmarks. |
Calibrated Language Model Fine-Tuning for In- and Out-of-Distribution Data (2020.emnlp-main)
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| Challenge: | Pre-trained language models suffer from severe miscalibration for both in-distribution and out-of-difference data due to over-parameterization. |
| Approach: | They propose a regularized method to improve in-distribution and out-of-distance calibrations by using on-manifold regularization and off-manfold regularisation. |
| Outcome: | The proposed method outperforms existing methods for text classification in terms of expectation calibration error, misclassification detection, and OOD detection on six datasets. |
Adversarial Regularization as Stackelberg Game: An Unrolled Optimization Approach (2021.emnlp-main)
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Simiao Zuo, Chen Liang, Haoming Jiang, Xiaodong Liu, Pengcheng He, Jianfeng Gao, Weizhu Chen, Tuo Zhao
| Challenge: | Existing approaches to adversarial regularization treat adversarials and defending players equally, which is undesirable because only the defending player contributes to the generalization performance. |
| Approach: | They propose a method which formulates adversarial regularization as a Stackelberg game and induces a competition between a leader and a follower. |
| Outcome: | The proposed method outperforms existing adversarial regularization baselines on a set of machine translation and natural language understanding tasks. |
Hephaestus: Improving Fundamental Agent Capabilities of Large Language Models through Continual Pre-Training (2025.naacl-long)
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Yuchen Zhuang, Jingfeng Yang, Haoming Jiang, Xin Liu, Kewei Cheng, Sanket Lokegaonkar, Yifan Gao, Qing Ping, Tianyi Liu, Binxuan Huang, Zheng Li, Zhengyang Wang, Pei Chen, Ruijie Wang, Rongzhi Zhang, Nasser Zalmout, Priyanka Nigam, Bing Yin, Chao Zhang
| Challenge: | Existing LLMs often rely on complex prompting or extensive fine-tuning to introduce new capabilities while preserving strong generalizability. |
| Approach: | They propose a large-scale pre-training corpus to enhance LLM agents' capabilities . they use 103B agent-specific data encompassing 76,537 APIs . |
| Outcome: | The proposed training corpus outperforms open-source LLMs and commercial LLM agents on three agent benchmarks. |
Multilingual Knowledge Graph Completion with Self-Supervised Adaptive Graph Alignment (2022.acl-long)
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Zijie Huang, Zheng Li, Haoming Jiang, Tianyu Cao, Hanqing Lu, Bing Yin, Karthik Subbian, Yizhou Sun, Wei Wang
| Challenge: | Existing methods to predict missing facts in knowledge graphs are limited in language alignment . SS-AGA uses seed alignment as an edge type to fuses all KGs as a whole graph . |
| Approach: | They propose a self-supervised adaptive graph alignment method that fuses all KGs as a whole graph by regarding alignment as 'a new edge type' they propose SS-AGA method that uses relation-aware attention weights to capture potential alignment pairs in a new paradigm. |
| Outcome: | The proposed method can predict missing facts in a knowledge graph (KG) but language alignment is scarce and new alignment identification is noisy. |
Knowledge-Selective Pretraining for Attribute Value Extraction (2023.findings-emnlp)
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Hui Liu, Qingyu Yin, Zhengyang Wang, Chenwei Zhang, Haoming Jiang, Yifan Gao, Zheng Li, Xian Li, Chao Zhang, Bing Yin, William Wang, Xiaodan Zhu
| Challenge: | Existing methods for AVE are limited on rare attributes due to poor generalization ability. |
| Approach: | They propose to leverage pretraining and transfer learning to address weaknesses in existing methods. |
| Outcome: | The proposed method achieves new state-of-the-art performance without pretraining on rare attributes with limited training resources. |
IHEval: Evaluating Language Models on Following the Instruction Hierarchy (2025.naacl-long)
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Zhihan Zhang, Shiyang Li, Zixuan Zhang, Xin Liu, Haoming Jiang, Xianfeng Tang, Yifan Gao, Zheng Li, Haodong Wang, Zhaoxuan Tan, Yichuan Li, Qingyu Yin, Bing Yin, Meng Jiang
| Challenge: | Instruction-tuned language models (LMs) are increasingly deployed as interactive services across various applications. |
| Approach: | They propose a benchmark to evaluate models' ability to follow the instruction hierarchy by comparing their models to a set of benchmarks. |
| Outcome: | The proposed benchmark covers 3,538 examples across nine tasks covering cases where instructions in different priorities either align or conflict. |
Token-wise Curriculum Learning for Neural Machine Translation (2021.findings-emnlp)
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| Challenge: | Existing curriculum learning approaches to Neural Machine Translation (NMT) require sampling sufficient amounts of “easy” samples from training data at the early stage of training. |
| Approach: | They propose a token-wise curriculum learning approach that creates sufficient amounts of easy samples from training data. |
| Outcome: | The proposed approach outperforms baselines on five language pairs on low-resource languages. |
Super Tickets in Pre-Trained Language Models: From Model Compression to Improving Generalization (2021.acl-long)
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Chen Liang, Simiao Zuo, Minshuo Chen, Haoming Jiang, Xiaodong Liu, Pengcheng He, Tuo Zhao, Weizhu Chen
| Challenge: | 'lottery tickets' can be trained to match the performance of a full model . subnetwork training can also outperform random sampled subnetworks of the same size . |
| Approach: | They propose to train a subnetwork of 'lottery tickets' to match the full model's performance. |
| Outcome: | The proposed model outperforms subnetworks of the same size in a phase transition phenomenon . the proposed model improves single task fine-tuning by 0.9 points on BERT-base and 1.0 points on GLUE large . |
Can Language Models Follow Multiple Turns of Entangled Instructions? (2025.findings-emnlp)
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Chi Han, Xin Liu, Haodong Wang, Shiyang Li, Jingfeng Yang, Haoming Jiang, Zhengyang Wang, Qingyu Yin, Liang Qiu, Changlong Yu, Yifan Gao, Zheng Li, Bing Yin, Jingbo Shang, Heng Ji
| Challenge: | Despite of significant achievements in improving instruction-following capabilities of large language models, the ability to process multiple potentially entangled or conflicting instructions remains a considerable challenge. |
| Approach: | They construct multi-turn instruction with 1.1K high-quality multi-turned conversations using the human-in-the-loop approach and examine their capabilities. |
| Outcome: | The proposed model shows that it is difficult to integrate multiple turns and balance competing objectives when instructions intersect or conflict. |
Improving Large Language Models Function Calling and Interpretability via Guided-Structured Templates (2025.emnlp-main)
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Hy Dang, Tianyi Liu, Zhuofeng Wu, Jingfeng Yang, Haoming Jiang, Tao Yang, Pei Chen, Zhengyang Wang, Helen Wang, Huasheng Li, Bing Yin, Meng Jiang
| Challenge: | Large language models (LLMs) have strong reasoning and tool-use capabilities, yet fail in real-world tool-interactions due to incorrect parameterization, poor tool selection, or misinterpretation of user intent. |
| Approach: | They propose a curriculum-inspired framework that leverages structured reasoning templates to guide LLMs through more deliberate step-by-step instructions for generating function calls. |
| Outcome: | The proposed framework reduces tool-use errors and improves interpretability and transparency of tool-using agents. |
ARCH: Efficient Adversarial Regularized Training with Caching (2021.findings-emnlp)
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Simiao Zuo, Chen Liang, Haoming Jiang, Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen, Tuo Zhao
| Challenge: | Existing approaches to regularize models require generating a perturbation for each sample in each epoch. |
| Approach: | They propose an adversarial regularization method where perturbations are generated and cached once every several epochs. |
| Outcome: | The proposed method significantly eases the computational burden (saves up to 70% of computational time) it produces a notably better (in most of the tasks) or comparable model generalization. |
Data Diversity Matters for Robust Instruction Tuning (2024.findings-emnlp)
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Alexander Bukharin, Shiyang Li, Zhengyang Wang, Jingfeng Yang, Bing Yin, Xian Li, Chao Zhang, Tuo Zhao, Haoming Jiang
| Challenge: | Recent studies have shown that by curating high quality and diverse instruction tuning datasets, we can significantly improve instruction-following capabilities. |
| Approach: | They propose an algorithm to control diversity and quality of instruction tuning datasets and validate it. |
| Outcome: | The proposed algorithm significantly improves worst and average case performance on large scale instruction tuning datasets. |
Named Entity Recognition with Small Strongly Labeled and Large Weakly Labeled Data (2021.acl-long)
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| Challenge: | Existing work focuses on learning deep NER models with weak supervision without any human annotation. |
| Approach: | They propose a framework that can suppress the noise of the weak labels and fine-tune over the strongly labeled data. |
| Outcome: | The proposed framework outperforms existing methods on Named Entity Recognition tasks with weak supervision and weakly labeled data. |
Towards Automatic Evaluation of Dialog Systems: A Model-Free Off-Policy Evaluation Approach (2021.emnlp-main)
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| Challenge: | Existing methods for evaluation of dialog systems are expensive and not scalable . a framework for estimating human evaluation scores is proposed to bridge this gap . |
| Approach: | They propose a framework for estimating human evaluation scores based on off-policy evaluation . they use language quality metrics for single-turn response generation given a fixed context . |
| Outcome: | The proposed framework outperforms existing methods in terms of correlation with human evaluation scores. |
DORM: Preference Data Weights Optimization for Reward Modeling in LLM Alignment (2025.findings-emnlp)
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Rongzhi Zhang, Chenwei Zhang, Xinyang Zhang, Liang Qiu, Haoming Jiang, Yuchen Zhuang, Qingru Zhang, Hyokun Yun, Xian Li, Bing Yin, Tuo Zhao, Chao Zhang
| Challenge: | Existing approaches to align large language models with human preferences are noisy and varying in importance of preference samples. |
| Approach: | a new method enhances reward modeling by learning to dynamically weigh preference data. |
| Outcome: | a new method improves the performance of large language models with human preferences . it initializes data importance and iteratively refines them to maximize validation performance. |
SEQZERO: Few-shot Compositional Semantic Parsing with Sequential Prompts and Zero-shot Models (2022.findings-naacl)
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| Challenge: | Recent research shows promising results on combining pretrained language models with canonical utterance for few-shot semantic parsing. |
| Approach: | They propose a few-shot semantic parsing method that decomposes a problem into a sequence of sub-problems, which correspond to the sub-clauses of the formal language. |
| Outcome: | The proposed method achieves SOTA performance of BART-based models on GeoQuery and EcommerceQuery, which are two few-shot datasets with compositional data split. |
Large Language Models Are Poor Clinical Decision-Makers: A Comprehensive Benchmark (2024.emnlp-main)
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Fenglin Liu, Zheng Li, Hongjian Zhou, Qingyu Yin, Jingfeng Yang, Xianfeng Tang, Chen Luo, Ming Zeng, Haoming Jiang, Yifan Gao, Priyanka Nigam, Sreyashi Nag, Bing Yin, Yining Hua, Xuan Zhou, Omid Rohanian, Anshul Thakur, Lei Clifton, David Clifton
| Challenge: | Existing studies focus on evaluating large language models in close-ended QA tasks, but many clinical decisions involve answering open-ended questions without pre-set options. |
| Approach: | They construct a benchmark to better understand large language models in the clinic . they use existing datasets to evaluate LLMs in clinical situations . |
| Outcome: | The proposed model outperforms human experts in multiple medical tasks. |
Fine-Tuning Pre-trained Language Model with Weak Supervision: A Contrastive-Regularized Self-Training Approach (2021.naacl-main)
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| Challenge: | Fine-tuned pre-trained language models (LMs) have enormous success in many natural language processing tasks, but they still require excessive labeled data in the fine-tuning stage. |
| Approach: | They propose a framework to enable fine-tuning pre-trained language models with weak supervision without any labeled data. |
| Outcome: | The proposed framework outperforms the strongest baseline and achieves competitive performance with fully-supervised fine-tuning methods. |
Contextual Text Denoising with Masked Language Model (D19-55)
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| Challenge: | Recent advances in NLP have been vulnerable to noisy inputs. |
| Approach: | They propose a contextual text denoising algorithm based on a ready-to-use masked language model that does not require retraining and can be integrated into any NLP system without additional training on paired cleaning training data. |
| Outcome: | The proposed algorithm can correct noise text and improve performance in several downstream tasks. |
IceBreaker for Conversational Agents: Breaking the First-Message Barrier with Personalized Starters (2026.acl-industry)
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Hongwei Zheng, Weiqi Wu, Zhengjia Wang, Guanyu Jiang, Haoming Li, Tianyu Wu, Yongchun Zhu, Jingwu Chen, Feng Zhang
| Challenge: | Existing efforts focus on activation within ongoing dialogues, while overlooking a key real-world bottleneck. |
| Approach: | They propose a conversation starter generation system that generates personalized starters to guide users into conversation without explicit user intent. |
| Outcome: | The proposed system improves user active days by +1.84 and click-through rate by +94.25 and has been deployed in production. |
Self-Training with Differentiable Teacher (2022.findings-naacl)
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| Challenge: | Existing methods for self-training are interpreted as teacher-student frameworks, where the teacher generates pseudo-labels and the student makes predictions. |
| Approach: | They propose a differentiable self-training method that treats teacher-student as a Stackelberg game where a leader is always in a more advantageous position than a follower. |
| Outcome: | The proposed model outperforms existing methods on semi- and weakly-supervised learning tasks on semi and weak supervised tasks. |
Context-Aware Query Rewriting for Improving Users’ Search Experience on E-commerce Websites (2023.acl-industry)
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| Challenge: | Existing query rewriting models ignore user history behaviors and consider only the instant search query, which is often a short string offering limited information about the true shopping intent. |
| Approach: | They propose an end-to-end context-aware query rewriting model that takes search context into account and builds a session graph using the history search queries and their contained words. |
| Outcome: | The proposed model outperforms state-of-the-art models under various metrics. |
Multi-Domain Neural Machine Translation with Word-Level Adaptive Layer-wise Domain Mixing (2020.acl-main)
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| Challenge: | Existing multi-domain neural machine translation models lack adaptation to individual domains. |
| Approach: | They propose a multi-domain neural machine translation model with individual modules for each domain . they use word-level, adaptive and layer-wise domain mixing to achieve this . |
| Outcome: | The proposed model outperforms existing models in several NMT tasks. |
SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization (2020.acl-main)
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| Challenge: | Existing methods for fine-tuning pre-trained models fail to generalize to unseen data. |
| Approach: | They propose a framework for robust and efficient fine-tuning for pre-trained models . proposed framework achieves new state-of-the-art performance on a number of NLP tasks . |
| Outcome: | The proposed framework outperforms the state-of-the-art T5 model on GLUE, SNLI, SciTail and ANLI. |