LoFa is a new benchmark and LFR@k metric for measuring LLM resistance to sustained logical fallacy attacks via generated question-argument pairs and debate simulations.
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4 Pith papers cite this work, alongside 3 external citations. Polarity classification is still indexing.
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A neuro-symbolic system using large reasoning models and model checkers outperforms dedicated reactive synthesis tools on benchmarks and handles parameterized systems.
LLMs handle LTL syntax better than semantics, improve with detailed prompts, and perform substantially better when the task is reframed as Python code completion.
citing papers explorer
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Truth or Sophistry? LoFa: A Benchmark for LLM Robustness Against Logical Fallacies
LoFa is a new benchmark and LFR@k metric for measuring LLM resistance to sustained logical fallacy attacks via generated question-argument pairs and debate simulations.
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Natural Synthesis: Outperforming Reactive Synthesis Tools with Large Reasoning Models
A neuro-symbolic system using large reasoning models and model checkers outperforms dedicated reactive synthesis tools on benchmarks and handles parameterized systems.
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Syntax Is Easy, Semantics Is Hard: Evaluating LLMs for LTL Translation
LLMs handle LTL syntax better than semantics, improve with detailed prompts, and perform substantially better when the task is reframed as Python code completion.
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