CRAFT is a Pareto-front prompt optimizer that allocates scarce LLM validation calls to candidates near the current front using accuracy- and cost-oriented generators plus NSGA-II retention.
Exchange-of-Thought: Enhancing Large Language Model Capabilities through Cross-Model Communication
3 Pith papers cite this work, alongside 18 external citations. Polarity classification is still indexing.
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DyLAN automatically selects and dynamically organizes LLM agents for collaboration, outperforming fixed-agent baselines on code generation, reasoning, and decision tasks with up to 25% accuracy gains on some MMLU subjects.
CRAFT is a unified bidirectional counterfactual reasoning framework that improves LLM performance on tabular QA and fact verification tasks over baselines on WikiTQ and TabFact.
citing papers explorer
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CRAFT: Cost-aware Refinement And Front-aware Tuning of Prompts
CRAFT is a Pareto-front prompt optimizer that allocates scarce LLM validation calls to candidates near the current front using accuracy- and cost-oriented generators plus NSGA-II retention.
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A Dynamic LLM-Powered Agent Network for Task-Oriented Agent Collaboration
DyLAN automatically selects and dynamically organizes LLM agents for collaboration, outperforming fixed-agent baselines on code generation, reasoning, and decision tasks with up to 25% accuracy gains on some MMLU subjects.
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CRAFT: A Unified Counterfactual Reasoning Framework for Tabular Question Answering and Fact Verification
CRAFT is a unified bidirectional counterfactual reasoning framework that improves LLM performance on tabular QA and fact verification tasks over baselines on WikiTQ and TabFact.