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Odunayo Ogundepo

7 accepted papers

2025

AfroBench: How Good are Large Language Models on African Languages?

ACL 2025finding

Large-scale multilingual evaluations, such as MEGA, often include only a handful of African languages due to the scarcity of high-qualityevaluation data and the limited discoverability of existing African datasets. This lack of representation hinders comprehensive LLM evaluation across a diverse ran…

2024

“Knowing When You Don’t Know”: A Multilingual Relevance Assessment Dataset for Robust Retrieval-Augmented Generation

EMNLP 2024finding

Retrieval-Augmented Generation (RAG) grounds Large Language Model (LLM) output by leveraging external knowledge sources to reduce factual hallucinations. However, prior work lacks a comprehensive evaluation of different language families, making it challenging to evaluate LLM robustness against erro…

2023

Better Quality Pre-training Data and T5 Models for African Languages

EMNLP 2023short main

In this study, we highlight the importance of enhancing the quality of pretraining data in multilingual language models. Existing web crawls have demonstrated quality issues, particularly in the context of low-resource languages. Consequently, we introduce a new multilingual pretraining corpus for…

Cited by 0SourcecodeScholar
2023

Cross-lingual Open-Retrieval Question Answering for African Languages

EMNLP 2023long findings

African languages have far less in-language content available digitally, making it challenging for question answering systems to satisfy the information needs of users. Cross-lingual open-retrieval question answering (XOR QA) systems -- those that retrieve answer content from other languages while s…

Cited by 0SourceScholar
2023

Evaluating Embedding APIs for Information Retrieval

ACL 2023industry

The ever-increasing size of language models curtails their widespread access to the community, thereby galvanizing many companies and startups into offering access to large language models through APIs. One particular API, suitable for dense retrieval, is the semantic embedding API that builds vecto…

Cited by 24SourcePDFScholar
2022

AfriCLIRMatrix: Enabling Cross-Lingual Information Retrieval for African Languages

EMNLP 2022main

Language diversity in NLP is critical in enabling the development of tools for a wide range of users.However, there are limited resources for building such tools for many languages, particularly those spoken in Africa.For search, most existing datasets feature few or no African languages, directly i…

2022

MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition

EMNLP 2022main

African languages are spoken by over a billion people, but they are under-represented in NLP research and development. Multiple challenges exist, including the limited availability of annotated training and evaluation datasets as well as the lack of understanding of which settings, languages, and re…