Papers by John Mendonça

2 papers
Soda-Eval: Open-Domain Dialogue Evaluation in the age of LLMs (2024.findings-emnlp)

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Challenge: Current evaluation practices of open domain dialogue systems are still highly dependent on human evaluation.
Approach: They propose to use an annotated dataset to evaluate chatbots using large language models.
Outcome: The proposed model improves over few-shot inferences on a GPT-3.5 generated dialogue dataset.
MEDAL: A Framework for Benchmarking LLMs as Multilingual Open-Domain Dialogue Evaluators (2026.findings-eacl)

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Challenge: Existing meta-evaluation benchmarks are static, outdated, and lacking in multilingual coverage.
Approach: They propose a framework for curating more representative open-domain dialogue evaluation benchmarks . they leverage several LLMs to generate user-chatbot multilingual dialogues conditioned on varied seed contexts based on a state-of-the-art LLM .
Outcome: The proposed framework exploits state-of-the-art LLMs to perform multilingual evaluations of open-domain chatbots.

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