3 citations · 4 across the 24 of their papers we have counts for
25 papers
Resource-Lean Lexicon Induction for German Dialects
Robert Litschko, Barbara Plank, Diego Frassinelli
Automatic induction of high-quality dictionaries is essential for building lexical resources, yet low-resource languages and dialects pose several challenges: limited access to ann…
Information Asymmetry across Language Varieties: A Case Study on Cantonese-Mandarin and Bavarian-German QA
Renhao Pei, Siyao Peng, Verena Blaschke +2
Large Language Models (LLMs) are becoming a common way for humans to seek knowledge, yet their coverage and reliability vary widely. Especially for local language varieties, there…
Decoupling the Effect of Chain-of-Thought Reasoning: A Human Label Variation Perspective
Beiduo Chen, Tiancheng Hu, Caiqi Zhang +3
Reasoning-tuned LLMs utilizing long Chain-of-Thought (CoT) excel at single-answer tasks, yet their ability to model Human Label Variation--which requires capturing probabilistic am…
Make Every Letter Count: Building Dialect Variation Dictionaries from Monolingual Corpora
Robert Litschko, Verena Blaschke, Diana Burkhardt +2
Dialects exhibit a substantial degree of variation due to the lack of a standard orthography. At the same time, the ability of Large Language Models (LLMs) to process dialects rema…
Evaluating Large Language Models for Cross-Lingual Retrieval
Longfei Zuo, Pingjun Hong, Oliver Kraus +2
Multi-stage information retrieval (IR) has become a widely-adopted paradigm in search. While Large Language Models (LLMs) have been extensively evaluated as second-stage reranking…
Reason to Rote: Rethinking Memorization in Reasoning
Yupei Du, Philipp Mondorf, Silvia Casola +3
Large language models readily memorize arbitrary training instances, such as label noise, yet they perform strikingly well on reasoning tasks. In this work, we investigate how lang…
Cross-Dialect Information Retrieval: Information Access in Low-Resource and High-Variance Languages
Robert Litschko, Oliver Kraus, Verena Blaschke +1
A large amount of local and culture-specific knowledge (e.g., people, traditions, food) can only be found in documents written in dialects. While there has been extensive research…
Exploring Large Language Models for Product Attribute Value Identification
Kassem Sabeh, Mouna Kacimi, Johann Gamper +2
Product attribute value identification (PAVI) involves automatically identifying attributes and their values from product information, enabling features like product search, recomm…
To Know or Not To Know? Analyzing Self-Consistency of Large Language Models under Ambiguity
Anastasiia Sedova, Robert Litschko, Diego Frassinelli +2
One of the major aspects contributing to the striking performance of large language models (LLMs) is the vast amount of factual knowledge accumulated during pre-training. Yet, many…
An Empirical Comparison of Generative Approaches for Product Attribute-Value Identification
Kassem Sabeh, Robert Litschko, Mouna Kacimi +2
Product attributes are crucial for e-commerce platforms, supporting applications like search, recommendation, and question answering. The task of Product Attribute and Value Identi…
"Seeing the Big through the Small": Can LLMs Approximate Human Judgment Distributions on NLI from a Few Explanations?
Beiduo Chen, Xinpeng Wang, Siyao Peng +3
Human label variation (HLV) is a valuable source of information that arises when multiple human annotators provide different labels for valid reasons. In Natural Language Inference…
MaiNLP at SemEval-2024 Task 1: Analyzing Source Language Selection in Cross-Lingual Textual Relatedness
Shijia Zhou, Huangyan Shan, Barbara Plank +1
This paper presents our system developed for the SemEval-2024 Task 1: Semantic Textual Relatedness (STR), on Track C: Cross-lingual. The task aims to detect semantic relatedness of…
Vicinal Risk Minimization for Few-Shot Cross-lingual Transfer in Abusive Language Detection
Gretel Liz De la Peña Sarracén, Paolo Rosso, Robert Litschko +2
Cross-lingual transfer learning from high-resource to medium and low-resource languages has shown encouraging results. However, the scarcity of resources in target languages remain…
Establishing Trustworthiness: Rethinking Tasks and Model Evaluation
Robert Litschko, Max Müller-Eberstein, Rob van der Goot +2
Language understanding is a multi-faceted cognitive capability, which the Natural Language Processing (NLP) community has striven to model computationally for decades. Traditionall…
Donkii: Can Annotation Error Detection Methods Find Errors in Instruction-Tuning Datasets?
Leon Weber-Genzel, Robert Litschko, Ekaterina Artemova +1
Instruction tuning has become an integral part of training pipelines for Large Language Models (LLMs) and has been shown to yield strong performance gains. In an orthogonal line of…
Boosting Zero-shot Cross-lingual Retrieval by Training on Artificially Code-Switched Data
Robert Litschko, Ekaterina Artemova, Barbara Plank
Transferring information retrieval (IR) models from a high-resource language (typically English) to other languages in a zero-shot fashion has become a widely adopted approach. In…
A General-Purpose Multilingual Document Encoder
Onur Galoğlu, Robert Litschko, Goran Glavaš
Massively multilingual pretrained transformers (MMTs) have tremendously pushed the state of the art on multilingual NLP and cross-lingual transfer of NLP models in particular. Whil…
ZusammenQA: Data Augmentation with Specialized Models for Cross-lingual Open-retrieval Question Answering System
Chia-Chien Hung, Tommaso Green, Robert Litschko +5
This paper introduces our proposed system for the MIA Shared Task on Cross-lingual Open-retrieval Question Answering (COQA). In this challenging scenario, given an input question t…
Parameter-Efficient Neural Reranking for Cross-Lingual and Multilingual Retrieval
Robert Litschko, Ivan Vulić, Goran Glavaš
State-of-the-art neural (re)rankers are notoriously data-hungry which -- given the lack of large-scale training data in languages other than English -- makes them rarely used in mu…
On Cross-Lingual Retrieval with Multilingual Text Encoders
Robert Litschko, Ivan Vulić, Simone Paolo Ponzetto +1
In this work we present a systematic empirical study focused on the suitability of the state-of-the-art multilingual encoders for cross-lingual document and sentence retrieval task…
Evaluating Multilingual Text Encoders for Unsupervised Cross-Lingual Retrieval
Robert Litschko, Ivan Vulić, Simone Paolo Ponzetto +1
Pretrained multilingual text encoders based on neural Transformer architectures, such as multilingual BERT (mBERT) and XLM, have achieved strong performance on a myriad of language…
Probing Pretrained Language Models for Lexical Semantics
Ivan Vulić, Edoardo Maria Ponti, Robert Litschko +2
The success of large pretrained language models (LMs) such as BERT and RoBERTa has sparked interest in probing their representations, in order to unveil what types of knowledge the…
Towards Instance-Level Parser Selection for Cross-Lingual Transfer of Dependency Parsers
Robert Litschko, Ivan Vulić, Željko Agić +1
Current methods of cross-lingual parser transfer focus on predicting the best parser for a low-resource target language globally, that is, "at treebank level". In this work, we pro…
How to (Properly) Evaluate Cross-Lingual Word Embeddings: On Strong Baselines, Comparative Analyses, and Some Misconceptions
Goran Glavas, Robert Litschko, Sebastian Ruder +1
Cross-lingual word embeddings (CLEs) enable multilingual modeling of meaning and facilitate cross-lingual transfer of NLP models. Despite their ubiquitous usage in downstream tasks…