DRACULA is the first dataset of user feedback on intermediate actions for deep research agents, showing that LLMs predict preferred actions better with full user history and that history-based action generation leads to higher user selection rates.
URL https://aclanthology.org/2021
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The base LLM choice dominates simulation outcomes in LLM-based social networks, while other design parameters show either additive or complex interactive effects.
A Bayesian ablation framework combined with information-theoretic metrics is introduced to analyze causal roles, distributedness, manifold complexity, and polysemanticity of task representations in neural networks.
citing papers explorer
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DRACULA: Hunting for the Actions Users Want Deep Research Agents to Execute
DRACULA is the first dataset of user feedback on intermediate actions for deep research agents, showing that LLMs predict preferred actions better with full user history and that history-based action generation leads to higher user selection rates.
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The $\textit{Silicon Society}$ Cookbook: Design Space of LLM-based Social Simulations
The base LLM choice dominates simulation outcomes in LLM-based social networks, while other design parameters show either additive or complex interactive effects.
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Understanding Task Representations in Neural Networks via Bayesian Ablation
A Bayesian ablation framework combined with information-theoretic metrics is introduced to analyze causal roles, distributedness, manifold complexity, and polysemanticity of task representations in neural networks.