17 citations · 71 across the 24 of their papers we have counts for
27 papers
APOLO: Automatic Prompt Optimization for Ontology Learning
Huu Tan Mai, Roman Kochnev, Cuong Xuan Chu +3
Ontology Learning (OL) from text has advanced with the emergence of Large Language Models (LLMs), but it remains challenging due to the limited availability of annotated training d…
Language Mixing in Reasoning Language Models: Patterns, Impact, and Internal Causes
Mingyang Wang, Lukas Lange, Heike Adel +3
Reasoning language models (RLMs) excel at complex tasks by leveraging a chain-of-thought process to generate structured intermediate steps. However, language mixing, i.e., reasonin…
Lost in Multilinguality: Dissecting Cross-lingual Factual Inconsistency in Transformer Language Models
Mingyang Wang, Heike Adel, Lukas Lange +4
Multilingual language models (MLMs) store factual knowledge across languages but often struggle to provide consistent responses to semantically equivalent prompts in different lang…
Bring Your Own Knowledge: A Survey of Methods for LLM Knowledge Expansion
Mingyang Wang, Alisa Stoll, Lukas Lange +3
Adapting large language models (LLMs) to new and diverse knowledge is essential for their lasting effectiveness in real-world applications. This survey provides an overview of stat…
Better Call SAUL: Fluent and Consistent Language Model Editing with Generation Regularization
Mingyang Wang, Lukas Lange, Heike Adel +2
To ensure large language models contain up-to-date knowledge, they need to be updated regularly. However, model editing is challenging as it might also affect knowledge that is unr…
Learn it or Leave it: Module Composition and Pruning for Continual Learning
Mingyang Wang, Heike Adel, Lukas Lange +2
In real-world environments, continual learning is essential for machine learning models, as they need to acquire new knowledge incrementally without forgetting what they have alrea…
Discourse-Aware In-Context Learning for Temporal Expression Normalization
Akash Kumar Gautam, Lukas Lange, Jannik Strötgen
Temporal expression (TE) normalization is a well-studied problem. However, the predominately used rule-based systems are highly restricted to specific settings, and upcoming machin…
Rehearsal-Free Modular and Compositional Continual Learning for Language Models
Mingyang Wang, Heike Adel, Lukas Lange +2
Continual learning aims at incrementally acquiring new knowledge while not forgetting existing knowledge. To overcome catastrophic forgetting, methods are either rehearsal-based, i…
GradSim: Gradient-Based Language Grouping for Effective Multilingual Training
Mingyang Wang, Heike Adel, Lukas Lange +2
Most languages of the world pose low-resource challenges to natural language processing models. With multilingual training, knowledge can be shared among languages. However, not al…
TADA: Efficient Task-Agnostic Domain Adaptation for Transformers
Chia-Chien Hung, Lukas Lange, Jannik Strötgen
Intermediate training of pre-trained transformer-based language models on domain-specific data leads to substantial gains for downstream tasks. To increase efficiency and prevent c…
NLNDE at SemEval-2023 Task 12: Adaptive Pretraining and Source Language Selection for Low-Resource Multilingual Sentiment Analysis
Mingyang Wang, Heike Adel, Lukas Lange +2
This paper describes our system developed for the SemEval-2023 Task 12 "Sentiment Analysis for Low-resource African Languages using Twitter Dataset". Sentiment analysis is one of t…
SwitchPrompt: Learning Domain-Specific Gated Soft Prompts for Classification in Low-Resource Domains
Koustava Goswami, Lukas Lange, Jun Araki +1
Prompting pre-trained language models leads to promising results across natural language processing tasks but is less effective when applied in low-resource domains, due to the dom…
Multilingual Normalization of Temporal Expressions with Masked Language Models
Lukas Lange, Jannik Strötgen, Heike Adel +1
The detection and normalization of temporal expressions is an important task and preprocessing step for many applications. However, prior work on normalization is rule-based, which…
CLIN-X: pre-trained language models and a study on cross-task transfer for concept extraction in the clinical domain
Lukas Lange, Heike Adel, Jannik Strötgen +1
The field of natural language processing (NLP) has recently seen a large change towards using pre-trained language models for solving almost any task. Despite showing great improve…
Boosting Transformers for Job Expression Extraction and Classification in a Low-Resource Setting
Lukas Lange, Heike Adel, Jannik Strötgen
In this paper, we explore possible improvements of transformer models in a low-resource setting. In particular, we present our approaches to tackle the first two of three subtasks…
To Share or not to Share: Predicting Sets of Sources for Model Transfer Learning
Lukas Lange, Jannik Strötgen, Heike Adel +1
In low-resource settings, model transfer can help to overcome a lack of labeled data for many tasks and domains. However, predicting useful transfer sources is a challenging proble…
ANEA: Distant Supervision for Low-Resource Named Entity Recognition
Michael A. Hedderich, Lukas Lange, Dietrich Klakow
Distant supervision allows obtaining labeled training corpora for low-resource settings where only limited hand-annotated data exists. However, to be used effectively, the distant…
NLNDE at CANTEMIST: Neural Sequence Labeling and Parsing Approaches for Clinical Concept Extraction
Lukas Lange, Xiang Dai, Heike Adel +1
The recognition and normalization of clinical information, such as tumor morphology mentions, is an important, but complex process consisting of multiple subtasks. In this paper, w…
A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios
Michael A. Hedderich, Lukas Lange, Heike Adel +2
Deep neural networks and huge language models are becoming omnipresent in natural language applications. As they are known for requiring large amounts of training data, there is a…
FAME: Feature-Based Adversarial Meta-Embeddings for Robust Input Representations
Lukas Lange, Heike Adel, Jannik Strötgen +1
Combining several embeddings typically improves performance in downstream tasks as different embeddings encode different information. It has been shown that even models using embed…
NLNDE: The Neither-Language-Nor-Domain-Experts' Way of Spanish Medical Document De-Identification
Lukas Lange, Heike Adel, Jannik Strötgen
Natural language processing has huge potential in the medical domain which recently led to a lot of research in this field. However, a prerequisite of secure processing of medical…
NLNDE: Enhancing Neural Sequence Taggers with Attention and Noisy Channel for Robust Pharmacological Entity Detection
Lukas Lange, Heike Adel, Jannik Strötgen
Named entity recognition has been extensively studied on English news texts. However, the transfer to other domains and languages is still a challenging problem. In this paper, we…
The SOFC-Exp Corpus and Neural Approaches to Information Extraction in the Materials Science Domain
Annemarie Friedrich, Heike Adel, Federico Tomazic +4
This paper presents a new challenging information extraction task in the domain of materials science. We develop an annotation scheme for marking information on experiments related…
Closing the Gap: Joint De-Identification and Concept Extraction in the Clinical Domain
Lukas Lange, Heike Adel, Jannik Strötgen
Exploiting natural language processing in the clinical domain requires de-identification, i.e., anonymization of personal information in texts. However, current research considers…