Legal2LogicICL improves accuracy and generalization when mapping legal cases to logical formulas by retrieving balanced diverse exemplars at semantic and structural levels, backed by the new Legal2Proleg dataset.
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Incremental visual scaffolding using multimodal models improves persistent common ground representation in situated dialogue by reducing representational blur compared to text-only approaches, with hybrid text-visual yielding best results on the IndiRef benchmark.
Passages made from high-convergence sentences improve LLM performance on inferential questions compared to cosine similarity selection.
citing papers explorer
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Legal2LogicICL: Improving Generalization in Transforming Legal Cases to Logical Formulas via Diverse Few-Shot Learning
Legal2LogicICL improves accuracy and generalization when mapping legal cases to logical formulas by retrieving balanced diverse exemplars at semantic and structural levels, backed by the new Legal2Proleg dataset.
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Using Machine Mental Imagery for Representing Common Ground in Situated Dialogue
Incremental visual scaffolding using multimodal models improves persistent common ground representation in situated dialogue by reducing representational blur compared to text-only approaches, with hybrid text-visual yielding best results on the IndiRef benchmark.
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Context Convergence Improves Answering Inferential Questions
Passages made from high-convergence sentences improve LLM performance on inferential questions compared to cosine similarity selection.