Papers by Juan Wisznia

2 papers
Are Optimal Algorithms Still Optimal? Rethinking Sorting in LLM-Based Pairwise Ranking with Batching and Caching (2025.acl-short)

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Challenge: a new framework for analyzing sorting algorithms in pairwise ranking prompting (PRP) is developed to re-center the cost model around LLM inferences rather than traditional pairwise comparisons.
Approach: They propose a framework for analyzing sorting algorithms in pairwise ranking prompting (PRP) they propose to re-center the cost model around LLM inferences rather than traditional pairwise comparisons.
Outcome: The proposed framework encourages strategies such as batching and caching to mitigate inference costs.
The Greatest Good Benchmark: Measuring LLMs’ Alignment with Utilitarian Moral Dilemmas (2024.emnlp-main)

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Challenge: Our analysis across 15 diverse LLMs reveals consistently encoded moral preferences that diverge from established moral theories and lay population moral standards.
Approach: They propose to evaluate the moral judgments of large language models using utilitarian dilemmas to determine their moral alignment.
Outcome: The findings highlight the ‘artificial moral compass’ of Large Language Models, offering insights into their moral alignment.

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