Papers by Ercong Nie
GNNavi: Navigating the Information Flow in Large Language Models by Graph Neural Network (2024.findings-acl)
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| Challenge: | Large Language Models (LLMs) exhibit strong In-Context Learning (ICL) capabilities when prompts with demonstrations are used. |
| Approach: | They propose a prompt-based parameter-efficient fine-tuning approach that leverages insights into ICL’s information flow dynamics and hardwires the desired information flow into the GNN. |
| Outcome: | The proposed approach surpasses prompt-based fine-tuning methods in few-shot settings by updating just 0.2% to 0.5% of parameters. |
Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning (2026.acl-long)
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Sikuan Yan, Xiufeng Yang, Zuchao Huang, Ercong Nie, Zifeng Ding, Zonggen Li, Xiaowen Ma, Jinhe Bi, Kristian Kersting, Jeff Z. Pan, Hinrich Schuetze, Volker Tresp, Yunpu Ma
| Challenge: | Large Language Models (LLMs) are stateless and limited by a finite context window, preventing them from maintaining knowledge across long conversations or evolving tasks. |
| Approach: | They propose a reinforcement learning framework that empowers LLMs to actively manage external memory through two specialized agents. |
| Outcome: | The proposed framework outperforms baselines and benchmarks across diverse question types, three benchmarks, and multiple model scales. |
Lost in Multilinguality: Dissecting Cross-lingual Factual Inconsistency in Transformer Language Models (2025.acl-long)
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| Challenge: | Multilingual language models store factual knowledge across languages but struggle to provide consistent responses to semantically equivalent prompts in different languages. |
| Approach: | They propose a linear shortcut method that bypasses computations in the final layers . this method improves accuracy and cross-lingual consistency . |
| Outcome: | The proposed method improves prediction accuracy and cross-lingual consistency. |
Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability in Large Language Models (2026.findings-acl)
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Hengyuan Zhang, Zhihao Zhang, Ercong Nie, Mingyang Wang, Zunhai Su, Yiwei Wang, Qianli Wang, Shuzhou Yuan, Xufeng Duan, Qibo Xue, Zeping Yu, Chenming Shang, Xiao Liang, Jing Xiong, Hui Shen, Chaofan Tao, Zhengwu Liu, Senjie Jin, Zhiheng Xi, Dongdong Zhang, Sophia Ananiadou, Tao Gui, Ruobing Xie, Hayden Kwok-Hay So, Hinrich Schuetze, Xuanjing Huang, Qi Zhang, Ngai Wong
| Challenge: | Existing literature on mechanistic interpretation (MI) treats it as an observational science, leaving practical applications underexplored. |
| Approach: | They propose a survey structured around the pipeline to identify and improve MI models. |
| Outcome: | The proposed framework enables tangible improvements in Alignment, Capability, and Efficiency. |
Decoding Probing: Revealing Internal Linguistic Structures in Neural Language Models Using Minimal Pairs (2024.lrec-main)
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| Challenge: | a new method is being developed to probe internal linguistic characteristics in neural language models layer by layer . |
| Approach: | They propose a method that uses minimal pairs benchmark to probe internal linguistic characteristics in neural language models layer by layer. |
| Outcome: | The proposed method captures grammaticality labels in language models layer by layer . it is based on the cognitive neurosciences of the brain and its representations as "neural activations". |
Cross-Lingual Retrieval Augmented Prompt for Low-Resource Languages (2023.findings-acl)
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| Challenge: | Multilingual pretrained language models (MPLMs) perform strongly in cross-lingual transfer. |
| Approach: | They propose to augment context with similar sentences retrieved from a high-resource language (HRL) they find a significant correlation between cross-lingual transfer performance and similarity between high- and low-resourced languages . |
| Outcome: | The proposed model outperforms finetuning by 3.7% on three downstream tasks with multilingual parallel test sets across 10 LRLs covering 6 language families in unlabeled and labeled settings. |
Large Language Models as Neurolinguistic Subjects: Discrepancy between Performance and Competence (2025.findings-acl)
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| Challenge: | Existing evaluations of Large Language Models (LLMs) reflect statistical rules that may not accurately represent LLMs’ true linguistic competence. |
| Approach: | They propose a method that combines minimal pair and diagnostic probing to analyze activation patterns across model layers. |
| Outcome: | The proposed method combines minimal pair and diagnostic probing to analyze activation patterns across model layers. |
Unleashing the Multilingual Encoder Potential: Boosting Zero-Shot Performance via Probability Calibration (2023.findings-emnlp)
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| Challenge: | Recent research demonstrates that multilingual encoder models are capable of zero-shot cross-lingual learning by using cloze-style prompts. |
| Approach: | They propose to reformulate input examples into cloze-style prompts to perform zero-shot multilingual tasks or linguistic probing by predicting label words at the masked token position. |
| Outcome: | The proposed method performs zero-shot multilingual tasks without updating parameters. |
SAD: A Large-Scale Strategic Argumentative Dialogue Dataset (2026.acl-long)
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YongKang Liu, Jiayang Yu, Mingyang Wang, Yiqun Zhang, Ercong Nie, Shi Feng, Daling Wang, Kaisong Song, Hinrich Schuetze
| Challenge: | Argumentation is a key part of human reasoning and decision-making . existing argumentative corpora focus on single-turn settings, but multi-turn dialogues are often realized as multi-turned dialogues . |
| Approach: | They present a dataset for strategic multi-turn argumentation dialogues . they annotate each utterance with five strategy types, allowing multiple strategies per utterrance . |
| Outcome: | The proposed dataset shows that explicit prompting improves fluency, stylistic coherence and persuasiveness. |
XToM: Exploring the Multilingual Theory of Mind for Large Language Models (2026.acl-long)
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Chunkit Chan, Yauwai Yim, Hongchuan Zeng, Zhiying Zou, Xinyuan Cheng, Zhifan Sun, Zheye Deng, Kawai Chung, Yuzhuo Ao, Fan Yixiang, Cheng Jiayang, Ercong Nie, Ginny Wong, Helmut Schmid, Hinrich Schuetze, Simon See, Yangqiu Song
| Challenge: | Existing evaluations of ToM in LLMs are limited to English, neglecting the linguistic diversity that shapes human cognition. |
| Approach: | They propose a multilingual benchmark that evaluates ToM across five languages . they find that models excel in multilingual language understanding, but their ToM performance varies across languages. |
| Outcome: | The proposed benchmark evaluates LLMs across five languages and incorporates diverse task scenarios. |
BMIKE-53: Investigating Cross-Lingual Knowledge Editing with In-Context Learning (2025.acl-long)
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| Challenge: | Using a benchmark for cross-lingual knowledge editing, knowledge editing is underexplored. |
| Approach: | They propose a benchmark for cross-lingual in-context knowledge editing that spans 53 languages and three KE datasets. |
| Outcome: | The proposed benchmark systematically evaluates cross-lingual knowledge editing (IKE) under zero-shot, one-shot and few-shot setups. |
Mechanistic Understanding and Mitigation of Language Confusion in English-Centric Large Language Models (2025.findings-emnlp)
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| Challenge: | Language confusion is a critical challenge for large language models, especially for English-centric models. |
| Approach: | They propose to combine behavioral benchmarking with neuron-level analysis to study language confusion. |
| Outcome: | The proposed approach matches multilingual alignment in confusion reduction for many languages and yields cleaner, higher-quality outputs. |
Tracing Multilingual Factual Knowledge Acquisition in Pretraining (2025.findings-emnlp)
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Yihong Liu, Mingyang Wang, Amir Hossein Kargaran, Felicia Körner, Ercong Nie, Barbara Plank, François Yvon, Hinrich Schuetze
| Challenge: | Large Language Models are capable of recalling multilingual factual knowledge, but most studies evaluate only the final model, leaving the development of factual recall and crosslingual consistency unexplored. |
| Approach: | They trace how factual recall and crosslingual consistency evolve during pretraining, focusing on OLMo-7B as a case study. |
| Outcome: | The results show that fact frequency is the key to a better recall of multilingual facts, regardless of language, and some low-frequency facts in non-English languages can still be correctly recalled. |
Look Within or Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning (2026.acl-long)
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| Challenge: | Parameter-Efficient Fine-Tuning (PEFT) is an alternative to Full-Parameter Fine-tuning, but its effectiveness on complex tasks such as reasoning and instruction-following remains unclear. |
| Approach: | They propose to use PEFT to reduce the number of trainable parameters while freezing the weights of LLMs. |
| Outcome: | The proposed methods perform well on standard tasks, but weaknesses on complex and adversarial settings call for new directions beyond current paradigms. |
PlaM: Training-Free Plateau-Guided Model Merging for Better Visual Grounding in MLLMs (2026.findings-acl)
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Zijing Wang, YongKang Liu, Mingyang Wang, Ercong Nie, Deyuan Chen, Zhengjie Zhao, Shi Feng, Daling Wang, Xiaocui Yang, Yifei Zhang, Hinrich Schuetze
| Challenge: | Multimodal instruction fine-tuning degrades textual reasoning capability, undermining multimodal performance. |
| Approach: | They propose a plateau-guided model merging method that selectively injects base language model parameters into MLLMs to mitigate this degradation. |
| Outcome: | The proposed framework reduces multimodal instruction fine-tuning degradation by incorporating a plateau-guided model merging method into MLLMs. |
ToPro: Token-Level Prompt Decomposition for Cross-Lingual Sequence Labeling Tasks (2024.eacl-long)
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| Challenge: | Prompt-based methods have been successfully applied to multilingual pretrained language models for zero-shot cross-lingual understanding. |
| Approach: | They propose a prompt-based method for token-level sequence labeling tasks . they propose to decompose an input sentence into single tokens and apply one prompt template to each token. |
| Outcome: | The proposed method outperforms Vanilla fine-tuning and Prompt-Tuning in zero-shot cross-lingual transfer . the method also attains state-of-the-art performance when employed with the mT5 model . |