Papers by Prasetya Ajie Utama
Towards Debiasing NLU Models from Unknown Biases (2020.emnlp-main)
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| Challenge: | Recent proposed debiasing methods rely on the assumption that the types of bias should be known a-priori, which limits their application to many NLU tasks and datasets. |
| Approach: | They propose a framework that prevents models from mainly utilizing biases without knowing them in advance. |
| Outcome: | The proposed framework allows existing methods to retain performance improvement on challenge datasets without specifically targeting biases. |
Mind the Trade-off: Debiasing NLU Models without Degrading the In-distribution Performance (2020.acl-main)
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| Challenge: | Recent studies show that pre-trained language models rely heavily on idiosyncratic biases of datasets. |
| Approach: | They propose a method which discourages models from exploiting biases while enabling them to receive enough incentive to learn from all the training examples. |
| Outcome: | The proposed method improves on out-of-distribution datasets while maintaining original in-district accuracy. |
Ranking Generated Summaries by Correctness: An Interesting but Challenging Application for Natural Language Inference (P19-1)
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| Challenge: | Recent advances on abstractive summarization have led to fluent summaries, but factual errors in generated summary still severely limit their use in practice. |
| Approach: | They evaluate summaries produced by state-of-the-art models via crowdsourcing and show that factual errors occur frequently. |
| Outcome: | The proposed models can detect errors and reduce them by reranking alternative summaries. |
Structured Dialogue Refinement: Building Retrieval-Augmented Question Answering on Goal-Oriented Dialogues (2026.findings-acl)
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| Challenge: | Retrieval-Augmented Generation (RAG) is widely used for knowledgeintensive question answering (QA), but a large amount of real-world problem-solving knowledge is captured in goal-oriented dialogues. |
| Approach: | They propose a framework that adapts dialogue corpora for RAG at both retrieval and generation stages without altering the underlying pipeline. |
| Outcome: | The proposed framework improves retrieval quality and QA performance under dialogue-specific structural challenges. |