Papers by Qianying Liu
Completely Modular Fine-tuning for Dynamic Language Adaptation (2026.findings-eacl)
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| Challenge: | Existing studies on multilingual fine-tuning with a fixed set of languages lack dynamic adaptability to new languages. |
| Approach: | They propose a modular fine-tuning pipeline that enables dynamic language adaptation for LLMs by first training English-centric adapters for each language separately and then merging them for arbitrary-direction translation. |
| Outcome: | The proposed pipeline achieves 86% performance over traditional fine-tuning on four languages, while training only 0.1% parameters and relying on English as a bridge language without catastrophic forgetting. |
Textual Enhanced Contrastive Learning for Solving Math Word Problems (2022.findings-emnlp)
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| Challenge: | Recent studies show that current models rely on shallow heuristics to predict solutions . a textual Enhanced Contrastive Learning framework enforces the models to distinguish semantically similar examples while holding different mathematical logic. |
| Approach: | They propose a textual Enhanced Contrastive Learning framework which enforces models to distinguish semantically similar examples while holding different mathematical logic. |
| Outcome: | The proposed framework improves on benchmark and challenge datasets in English and Chinese. |
ComSearch: Equation Searching with Combinatorial Strategy for Solving Math Word Problems with Weak Supervision (2023.eacl-main)
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| Challenge: | Existing weakly-supervised methods for solving math word problems are expensive and time-consuming. |
| Approach: | They propose a weakly-supervised approach to solve math word problems . they propose 'comsearch' algorithm which compresses the search space by excluding mathematically equivalent equations. |
| Outcome: | The proposed algorithm can compress the search space by excluding mathematically equivalent equations. |
Memorization, Emergence, and Explaining Reversal Failures: A Controlled Study of Relational Semantics in LLMs (2026.acl-long)
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Yihua Zhu, Qianying Liu, Jiaxin Wang, Fei Cheng, Chaoran Liu, Akiko Aizawa, Sadao Kurohashi, Hidetoshi Shimodaira
| Challenge: | Autoregressive LLMs perform well on relational tasks that require linking entities via relational words, but it is unclear whether they learn the logical semantics of such relations or whether left-to-right order bias is involved. |
| Approach: | They propose a framework that generates text from symmetric/inverse triples and trains autoregressive models from scratch. |
| Outcome: | The proposed framework generates text from symmetric/inverse triples, trains autoregressive models from scratch, and evaluates memorization, logical inference, and in-context generalization to unseen entities. |
Adversarial Speech Generation and Natural Speech Recovery for Speech Content Protection (2022.lrec-1)
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| Challenge: | Currently, researchers focus on how to protect the speaker's identifiable information, represented as voiceprint, contained in the speech. |
| Approach: | They propose a frame-by-frame adversarial speech generation system to protect speech . they build an adversarials-based method that converts adversarially generated speech to human speech. |
| Outcome: | The proposed method can encode and recover any sensitive audio, and it is easy to be conducted with publicly available speech recognition technology. |
Seeking Diverse Reasoning Logic: Controlled Equation Expression Generation for Solving Math Word Problems (2022.aacl-short)
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| Challenge: | Existing methods to solve Math Word Problems rely on human annotation . empirical results suggest that our method universally improves the performance on single-unknown and multiple-un unknown benchmarks. |
| Approach: | They propose a controlled equation generation solver by leveraging a set of control codes to guide the model to consider certain reasoning logic and decode the corresponding equations expressions transformed from the human reference. |
| Outcome: | The proposed method improves performance on single-unknown and multiple-un unknown benchmarks with 13.2% accuracy on the challenging multiple-unequal datasets. |
What Language Do Non-English-Centric Large Language Models Think in? (2025.findings-acl)
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Chengzhi Zhong, Qianying Liu, Fei Cheng, Junfeng Jiang, Zhen Wan, Chenhui Chu, Yugo Murawaki, Sadao Kurohashi
| Challenge: | Despite their robust performance in English, these models often exhibit reduced proficiency in non-English languages, and their outputs may reflect an inherent bias toward English-centric perspectives. |
| Approach: | They categorize non-English-centric large language models into two groups: CPMs and BLMs, which are pre-trained on a balanced mix of multiple languages from scratch. |
| Outcome: | The proposed models exhibit a pronounced internal preference for English tokens when projected into the vocabulary space. |
Relation Extraction with Weighted Contrastive Pre-training on Distant Supervision (2023.findings-eacl)
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| Challenge: | Existing methods ignore the intrinsic noise of distant supervision during the pre-training stage. |
| Approach: | They propose a weighted contrastive learning method that explicitly reduces noise . they leverage supervised data to estimate reliability and reduce noise compared to non-weighted baselines . |
| Outcome: | The proposed method reduces the noise of distant supervision and estimates reliability of pre-training instances. |
Rescue Implicit and Long-tail Cases: Nearest Neighbor Relation Extraction (2022.emnlp-main)
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| Challenge: | Existing RE models are incapable of handling implicit expressions and long-tail relation types due to language complexity and data sparsity. |
| Approach: | They propose a method to enhance relation extraction using k nearest neighbors (kNN-RE) kNN is a nearest-neighbor search tool that allows the model to consult training relations at test time . |
| Outcome: | The proposed model outperforms the best model to date on ACE05, SciERC, and Wiki80 datasets and outperformed the best on i2b2 and Wik80 dataset. |
Language Lives in Sparse Dimensions: Toward Interpretable and Efficient Multilingual Control for Large Language Models (2026.eacl-long)
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| Challenge: | Prior studies show that large language models map multilingual content into English-aligned representations at intermediate layers before projecting them back into target-language token spaces in the later layers. |
| Approach: | They propose a method to identify and manipulate dimensions that are sparse and sparsity-based . they propose to use as few as 50 sentences of either parallel or monolingual data to manipulate these dimensions . |
| Outcome: | Experiments on a multilingual generation control task show the interpretability of these dimensions. |
7 Points to Tsinghua but 10 Points to ? Assessing Large Language Models in Agentic Multilingual National Bias (2025.findings-acl)
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| Challenge: | Large Language Models have garnered significant attention for their capabilities in multilingual natural language processing, but studies on risks associated with cross biases are limited to immediate context preferences. |
| Approach: | They investigate multilingual bias in state-of-the-art Large Language Models by analyzing their responses to decision-making tasks across multiple languages. |
| Outcome: | The proposed model can provide personalized advice across university applications, travel, and relocation scenarios. |
Tree-structured Decoding for Solving Math Word Problems (D19-1)
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| Challenge: | Existing approaches to solve math word problems do not consider an abstract syntax tree. |
| Approach: | They propose a tree-structured decoding method that generates an abstract syntax tree of an equation in a top-down manner and can stop during decoding without a redundant stop token. |
| Outcome: | The proposed method achieves state-of-the-art performance on the largest dataset on this task. |
Inductive Relation Prediction with Logical Reasoning Using Contrastive Representations (2022.emnlp-main)
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| Challenge: | Existing methods for relation prediction in knowledge graphs (KGs) are limited by the inductive setting because entities in training process are finite. |
| Approach: | They propose a graph convolutional network-based model LogCo with logical reasoning by contrastive representations that extracts subgraphs and relational paths between two entities to supply the entity-independence. |
| Outcome: | The proposed model outperforms existing methods on twelve inductive datasets. |
Minimize Exposure Bias of Seq2Seq Models in Joint Entity and Relation Extraction (2020.findings-emnlp)
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Ranran Haoran Zhang, Qianying Liu, Aysa Xuemo Fan, Heng Ji, Daojian Zeng, Fei Cheng, Daisuke Kawahara, Sadao Kurohashi
| Challenge: | Existing methods to extract relation triplets from plain text introduce exposure bias . prior work has focused on pipeline methods that ignore intrinsic interactions between subtasks and propagate classification errors through the tasks. |
| Approach: | They propose a model that reduces the decoding length to three within a triplet and removes the order among triplets. |
| Outcome: | The proposed model overfits to both datasets while showing better generalization. |
GPT-RE: In-context Learning for Relation Extraction using Large Language Models (2023.emnlp-main)
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| Challenge: | Existing approaches to in-context learning (ICL) are lacking in relation extraction (RE) . emergence of large language models (LLMs) such as GPT-3 represents a significant advancement in natural language processing. |
| Approach: | They propose to incorporate task-aware representations into demonstration retrieval and enrich the demonstrations with gold label-induced reasoning logic. |
| Outcome: | The proposed model achieves SOTA and competitive performances on the Semeval and SciERC datasets. |
Exploring the Impact of Layer Normalization for Zero-shot Neural Machine Translation (2023.acl-short)
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| Challenge: | Recent studies have shown that layer normalization (LayerNorm) overfits training data and therefore has low generalizability for ZST. |
| Approach: | They propose to use the Transformer architecture to set the default layer normalization setting for zero-shot translation (ZST) they also propose to set LayerNorm after residual connections to outperform PreNorm by 12.3 BLEU points. |
| Outcome: | The proposed model outperforms the current model by 12.3 BLEU points on 54 directions on OPUS, IWSLT, and Europarl datasets. |
Shall We Team Up: Exploring Spontaneous Cooperation of Competing LLM Agents (2024.findings-emnlp)
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Zengqing Wu, Run Peng, Shuyuan Zheng, Qianying Liu, Xu Han, Brian Kwon, Makoto Onizuka, Shaojie Tang, Chuan Xiao
| Challenge: | Large Language Models (LLMs) are increasingly used in social simulations, where they are guided by carefully crafted instructions to exhibit human-like behaviors. |
| Approach: | They propose to use Large Language Models (LLMs) as agents to simulate the gradual transition from non-cooperative to cooperative behaviors of agents. |
| Outcome: | The proposed model can simulate the gradual transition from non-cooperative to cooperative behaviors in three competitive scenarios. |