Papers by Luke Yoffe

2 papers
FETA: A Benchmark for Few-Sample Task Transfer in Open-Domain Dialogue (2022.emnlp-main)

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Challenge: Prior studies of task transfer in dialogue consider only 2-4 tasks, focus on multitasks.
Approach: They propose a benchmark for FEw-sample TAsk transfer in open-domain dialogue.
Outcome: The proposed benchmark analyzes the transferability between 132 source-target task pairs and provides a baseline for future work.
DebUnc: Improving Large Language Model Agent Communication With Uncertainty Metrics (2025.findings-emnlp)

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Challenge: Multi-agent debates can improve the accuracy of Large Language Models by having multiple agents discuss solutions over several rounds of debate.
Approach: a debate framework that uses uncertainty metrics to assess agent confidence is proposed . the framework uses textual prompts or a modified attention mechanism that adjusts token weights .
Outcome: The proposed framework assesses agent confidence using uncertainty metrics . the framework is available at https://github.com/lukeyoffe/debunc.

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