Daily Arxiv

This is a page that curates AI-related papers published worldwide.
All content here is summarized using Google Gemini and operated on a non-profit basis.
Copyright for each paper belongs to the authors and their institutions; please make sure to credit the source when sharing.

Adaptive Elicitation of Latent Information Using Natural Language

Created by
  • Haebom

Author

Jimmy Wang, Thomas Zollo, Richard Zemel, Hongseok Namkoong

Outline

In this paper, we present an adaptive questioning strategy that gathers information to reduce uncertainty about potential entities. By leveraging the generalization ability and world knowledge of large-scale language models (LLMs), we quantify uncertainty through a meta-learned language model that simulates future observations. Using autoregressive forward simulations, we quantify how much new questions reduce epistemic uncertainty, and develop a sophisticated information-gathering strategy that selects the most informative next question. We demonstrate that it outperforms existing methods in experiments on a 20-question game, dynamic polling, and adaptive student assessment.

Takeaways, Limitations

•
Takeaways:
◦
Empirically demonstrating the effectiveness of adaptive information gathering strategies using LLM.
◦
We present a method to effectively quantify uncertainty in complex natural language situations using a meta-learning-based language model.
◦
The possibility of optimizing information gathering strategies is demonstrated through autoregressive forward simulations.
◦
Suggests applicability to various application fields (student assessment, disease diagnosis, user preference learning, etc.).
•
Limitations:
◦
Lack of detailed description of training data and structure of meta-learned language models.
◦
Further verification of generalizability is needed due to the special nature of the experimental environment.
◦
There is a need to improve the efficiency of the complex and costly question generation process.
◦
Lack of a clear solution to the difficulty of probabilistic modeling of abstract latent entities.
👍
Made with Slashpage