Wondimagegnhue Tufa


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Confidently Wrong: Exploring the Calibration and Expression of (Un)Certainty of Large Language Models in a Multilingual Setting
Lea Krause | Wondimagegnhue Tufa | Selene Baez Santamaria | Angel Daza | Urja Khurana | Piek Vossen
Proceedings of the Workshop on Multimodal, Multilingual Natural Language Generation and Multilingual WebNLG Challenge (MM-NLG 2023)

While the fluency and coherence of Large Language Models (LLMs) in text generation have seen significant improvements, their competency in generating appropriate expressions of uncertainty remains limited.Using a multilingual closed-book QA task and GPT-3.5, we explore how well LLMs are calibrated and express certainty across a diverse set of languages, including low-resource settings. Our results reveal strong performance in high-resource languages but a marked decline in performance in lower-resource languages. Across all, we observe an exaggerated expression of confidence in the model, which does not align with the correctness or likelihood of its responses. Our findings highlight the need for further research into accurate calibration of LLMs especially in a multilingual setting.

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A WordNet View on Crosslingual Transformers
Wondimagegnhue Tufa | Lisa Beinborn | Piek Vossen
Proceedings of the 12th Global Wordnet Conference

WordNet is a database that represents relations between words and concepts as an abstraction of the contexts in which words are used. Contextualized language models represent words in contexts but leave the underlying concepts implicit. In this paper, we investigate how different layers of a pre-trained language model shape the abstract lexical relationship toward the actual contextual concept. Can we define the amount of contextualized concept forming needed given the abstracted representation of a word? Specifically, we consider samples of words with different polysemy profiles shared across three languages, assuming that words with a different polysemy profile require a different degree of concept shaping by context. We conduct probing experiments to investigate the impact of prior polysemy profiles on the representation in different layers. We analyze how contextualized models can approximate meaning through context and examine crosslingual interference effects.


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SeqL at SemEval-2022 Task 11: An Ensemble of Transformer Based Models for Complex Named Entity Recognition Task
Fadi Hassan | Wondimagegnhue Tufa | Guillem Collell | Piek Vossen | Lisa Beinborn | Adrian Flanagan | Kuan Eeik Tan
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)

This paper presents our system used to participate in task 11 (MultiCONER) of the SemEval 2022 competition. Our system ranked fourth place in track 12 (Multilingual) and fifth place in track 13 (Code-Mixed). The goal of track 12 is to detect complex named entities in a multilingual setting, while track 13 is dedicated to detecting complex named entities in a code-mixed setting. Both systems were developed using transformer-based language models. We used an ensemble of XLM-RoBERTa-large and Microsoft/infoxlm-large with a Conditional Random Field (CRF) layer. In addition, we describe the algorithms employed to train our models and our hyper-parameter selection. We furthermore study the impact of different methods to aggregate the outputs of the individual models that compose our ensemble. Finally, we present an extensive analysis of the results and errors.