Papers by Prasant Mohapatra
Revisiting Zero-Shot Abstractive Summarization in the Era of Large Language Models from the Perspective of Position Bias (2024.naacl-short)
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| Challenge: | Position bias is a tendency of a model unfairly prioritizing information from certain parts of the input text over others, leading to undesirable behavior. |
| Approach: | They propose to measure position bias in large language models for zero-shot summarization tasks by measuring position bias. |
| Outcome: | The proposed model performance and position biases lead to new insights and discussion on zero-shot summarization tasks. |
Assessing LLMs for Zero-shot Abstractive Summarization Through the Lens of Relevance Paraphrasing (2025.findings-naacl)
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| Challenge: | Large Language Models (LLMs) have achieved state-of-the-art performance at zero-shot summarization of abstractive summaries for given articles, but little is known about their robustness at this task. |
| Approach: | They propose a strategy that uses the most relevant sentences to generate an ideal summary and then paraphrases them to obtain a minimally perturbed dataset. |
| Outcome: | The proposed approach can be used to measure the robustness of LLMs as summarizers on a minimally perturbed dataset. |