Papers by Helmut Schmid
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. |
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. |
CUTE: Measuring LLMs’ Understanding of Their Tokens (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) perform well on a wide variety of tasks, authors say . they lack direct access to characters, which can be difficult to generalize to new languages . |
| Approach: | They propose a benchmark to test the orthographic knowledge of Large Language Models . they find that most LLMs seem to know the spelling of their tokens - yet fail to manipulate text . |
| Outcome: | The proposed benchmark tests the orthographic knowledge of large language models . it finds that most LLMs seem to know the spelling of their tokens, but fail to manipulate text . |
Why don’t people use character-level machine translation? (2022.findings-acl)
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| Challenge: | despite evidence character-level systems are comparable with subword systems, they are rarely used in competitive setups in machine translation competitions. |
| Approach: | They propose a two-step decoder architecture that does not suffer from a slow-down due to the length of character sequences. |
| Outcome: | The proposed character-level MT systems show better domain robustness and better morphological generalization . the proposed decoder architecture shows no slow-down due to the length of character sequences . |
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. |
EXECUTE: A Multilingual Benchmark for LLM Token Understanding (2025.findings-acl)
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| Challenge: | EXECUTE is an expandable X(Cross)-Lingual Extension of CUTE that can be expanded to any language. |
| Approach: | They extend the CUTE benchmark to more languages with diverse scripts and writing systems, introducing EXECUTE. |
| Outcome: | The extended framework allows expansion to any language. |
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. |
Glot500: Scaling Multilingual Corpora and Language Models to 500 Languages (2023.acl-long)
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Ayyoob ImaniGooghari, Peiqin Lin, Amir Hossein Kargaran, Silvia Severini, Masoud Jalili Sabet, Nora Kassner, Chunlan Ma, Helmut Schmid, André Martins, François Yvon, Hinrich Schütze
| Challenge: | Lack of LLMs supporting low-resource languages is a serious impediment to bringing NLP to all of the world. |
| Approach: | They create a model that scales LLMs horizontally and a corpus that covers 511 low-resource languages. |
| Outcome: | The proposed model improves on five diverse tasks across low- and high-resource languages. |
Automatically Identifying Words That Can Serve as Labels for Few-Shot Text Classification (2020.coling-main)
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| Challenge: | Existing approaches to few-shot text classification require domain expertise and an understanding of the language model's abilities to define the mapping between words and labels. |
| Approach: | They propose a method that converts textual inputs to cloze questions that contain some form of task description and processes them with a pretrained language model to map the predicted words to labels. |
| Outcome: | The proposed approach performs almost as well as hand-crafted label-to-word mappings for a number of tasks with small amounts of training data. |
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 . |