Papers by Yijia Liu
Reinforcement Learning for Diffusion LLMs via Energy-Based Gibbs Alignment (2026.acl-long)
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| Challenge: | Diffusion Large Language Models (dLLMs) offer parallel decoding and bidirectional context modeling . aligning dLLms with reinforcement learning (RL) remains a challenge . |
| Approach: | They propose a variational framework that reformulates RL for dLLMs as a distribution matching problem. |
| Outcome: | The proposed framework reformulates RL for dLLMs as a distribution matching problem. |
Entity-Consistent End-to-end Task-Oriented Dialogue System with KB Retriever (D19-1)
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| Challenge: | Existing work on sequence-to-sequence dialogues treats the KB query as an attention over the entire KB without the guarantee that the generated entities are consistent with each other. |
| Approach: | They propose a framework which queries the knowledge base in two steps to improve consistency . they first return the most relevant KB row given a dialogue history . |
| Outcome: | The proposed framework outperforms baseline models and produces entity-consistent responses. |
Continual Training of Language Models for Few-Shot Learning (2022.emnlp-main)
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| Challenge: | Recent work on applying large language models (LMs) achieves impressive performance in many NLP applications. |
| Approach: | They propose to continuously post-train an LM with unlabeled domains to expand its knowledge without forgetting previous skills. |
| Outcome: | The proposed system improves few-shot end-task learning in these domains. |
Sequence-to-Sequence Learning for Task-oriented Dialogue with Dialogue State Representation (C18-1)
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| Challenge: | Existing pipeline models for task-oriented dialogue system require explicit modeling of dialogue states and hand-crafted action spaces to query domain-specific knowledge base. |
| Approach: | They propose a framework that leverages the advantages of classic pipeline and sequence-to-sequence models. |
| Outcome: | The proposed framework outperforms baseline models on automatic and human evaluation on a Stanford Multi-turn Multi-domain task-oriented dialogue dataset. |
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. |
LLM-Driven Implicit Target Augmentation and Fine-Grained Contextual Modeling for Zero-Shot and Few-Shot Stance Detection (2025.emnlp-main)
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| Challenge: | Recent studies on zero-shot and few-shot stance detection neglect implicit yet semantically important targets. |
| Approach: | They propose a framework that uses Large Language Models to annotate implicit targets . they also propose 'DyMCA' to dynamically adjust text-target contributions based on context . |
| Outcome: | The proposed framework achieves state-of-the-art on a benchmark dataset. |
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 . |
An AMR Aligner Tuned by Transition-based Parser (D18-1)
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| Challenge: | Experimental results show that our AMR aligner outperforms the rule-based aligner by achieving higher alignment F1 score and consistently improving two open-source AMR parsers. |
| Approach: | They propose a rich resource enhanced AMR aligner which produces multiple alignments and a new transition system for AMR parsing along with its oracle parser. |
| Outcome: | The proposed AMR aligner outperforms the current state-of-the-art parser by achieving higher alignment F1 score and consistently improving two open-source AMR parsers. |
Nash-Pruned CredMAS: Dynamic Panel Pruning for VLM-MAS using Nash-based Selection and Doubly-Robust Credits (2026.findings-acl)
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| Challenge: | Multi-Agent Systems (MAS) are expensive due to static panel designs, where all N agents communicate at every T round. |
| Approach: | They propose an economic framework that transforms agent selection into a dynamic resource allocation game. |
| Outcome: | The proposed system reduces token consumption by over 25% on challenging benchmarks while reducing token consumption. |
Distilling Knowledge for Search-based Structured Prediction (P18-1)
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| Challenge: | Existing studies have focused on the performance of structured prediction models, but they are often limited by the ambiguities of the reference policy. |
| Approach: | They propose to distill an ensemble of multiple models trained with different initializations into a single model and use it to explore the search space. |
| Outcome: | The proposed model outperforms the greedy models on two typical search-based structured prediction tasks and achieves 1.32 in LAS and 2.65 in BLEU over strong baselines. |
Class-Incremental Learning based on Label Generation (2023.acl-short)
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| Challenge: | Existing studies on pre-trained language models focus on task-incremental learning (TIL) but they perform poorly in a more challenging setting of class-incremental learning. |
| Approach: | They propose a method which solves CIL based on label generation by using sparse vocabulary and creates pseudo-replay samples by using label semantics. |
| Outcome: | The proposed method outperforms baseline models by a large margin in the class-incremental learning setting. |
Large Language Models Can Be Contextual Privacy Protection Learners (2024.emnlp-main)
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Yijia Xiao, Yiqiao Jin, Yushi Bai, Yue Wu, Xianjun Yang, Xiao Luo, Wenchao Yu, Xujiang Zhao, Yanchi Liu, Quanquan Gu, Haifeng Chen, Wei Wang, Wei Cheng
| Challenge: | Large Language Models (LLMs) have demonstrated remarkable linguistic comprehension and generation capability, but when applied to specialized industries, they face challenges such as hallucination, insufficient domain knowledge, and failing to incorporate the latest domain knowledge. |
| Approach: | They propose a paradigm for fine-tuning LLMs that effectively injects domain-specific knowledge while safeguarding inference-time data privacy. |
| Outcome: | The proposed model protects private data while enhancing the model's knowledge. |
Cross-Lingual BERT Transformation for Zero-Shot Dependency Parsing (D19-1)
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| Challenge: | Existing approaches to learn cross-lingual word embeddings in a contextual space are lacking. |
| Approach: | They propose a method to generate cross-lingual contextualized word embeddings using pre-trained BERT models by learning a linear transformation from contextual word alignments. |
| Outcome: | The proposed approach outperforms state-of-the-art models on zero-shot cross-lingual transfer parsing and is highly competitive with existing models. |
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. |
Adapting a Language Model While Preserving its General Knowledge (2022.emnlp-main)
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| Challenge: | Existing DA-training methods do not explicitly identify what knowledge should be preserved and what should be changed by the domain corpus. |
| Approach: | They propose to use an unlabeled corpus of aparticular domain to train a pre-trained general-purpose language model to adapt the LM so that end-tasks in the domain can give improved performances. |
| Outcome: | The proposed method improves the performance of pre-trained general-purpose language models by contrasting the representations of the general and the full (both general and domain knowledge) to learn an integrated representation with both general and specific knowledge. |
Parsing Tweets into Universal Dependencies (N18-1)
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| Challenge: | a new tweet treebank for English is designed to analyze tweets with universal dependencies (UD). |
| Approach: | They extend the universal dependencies guidelines to include special constructions in tweets that affect tokenization, part-of-speech tagging, and labeled dependencies. |
| Outcome: | The proposed method outperforms state-of-the-art parsers on other treebanks in accuracy and speed. |
Geneverse: A Collection of Open-source Multimodal Large Language Models for Genomic and Proteomic Research (2024.findings-emnlp)
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| Challenge: | generative Large Language Models (LLMs) are a promising tool for biomedical and healthcare research. |
| Approach: | They propose to use finetuned LLMs and multimodal LLM for genomic and proteomics tasks. |
| Outcome: | The proposed models outperform closed-source models in genomic and proteomics tasks and are highly accurate. |
VECO: Variable and Flexible Cross-lingual Pre-training for Language Understanding and Generation (2021.acl-long)
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| Challenge: | Existing work in multilingual pretraining relies on the shared vocabulary and bilingual contexts to encourage the correlation across languages. |
| Approach: | They propose to plug a cross-attention module into a Transformer encoder to explicitly build the interdependence between languages. |
| Outcome: | The proposed model outperforms existing models on XTREME and English-to-French translation datasets. |
Lattice-BERT: Leveraging Multi-Granularity Representations in Chinese Pre-trained Language Models (2021.naacl-main)
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| Challenge: | Pre-trained language models process text as a sequence of characters, ignoring more coarse granularity, e.g., words. |
| Approach: | They propose a new pre-training paradigm for Chinese that incorporates word representations along with characters and can model a sentence in a multi-granular manner. |
| Outcome: | The proposed model can bring an average increase of 1.5% under the 12-layer setting, which achieves new state-of-the-art among base-size models on the CLUE benchmarks. |