In-context learning emerges as implicit Bayesian inference of latent concepts when pretraining data has long-range coherence, proven for mixture-of-HMM distributions and replicated on the synthetic GINC dataset.
Learning to Ask Good Questions: Ranking Clarification Questions using Neural Expected Value of Perfect Information
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
abstract
Inquiry is fundamental to communication, and machines cannot effectively collaborate with humans unless they can ask questions. In this work, we build a neural network model for the task of ranking clarification questions. Our model is inspired by the idea of expected value of perfect information: a good question is one whose expected answer will be useful. We study this problem using data from StackExchange, a plentiful online resource in which people routinely ask clarifying questions to posts so that they can better offer assistance to the original poster. We create a dataset of clarification questions consisting of ~77K posts paired with a clarification question (and answer) from three domains of StackExchange: askubuntu, unix and superuser. We evaluate our model on 500 samples of this dataset against expert human judgments and demonstrate significant improvements over controlled baselines.
representative citing papers
Introduces NoisyToolBench benchmark and Ask-when-Needed framework to improve LLM tool-use performance when user instructions are unclear or incomplete.
The paper introduces an Information Gain Reward to train clarification behavior in LLM agents, reporting a 3.7% success rate gain over no-clarification baselines in τ-Bench evaluations across five models with minimal added steps.
A rationale is presented for developing an assistant in Minecraft to advance natural language understanding and dialogue learning.
citing papers explorer
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An Explanation of In-context Learning as Implicit Bayesian Inference
In-context learning emerges as implicit Bayesian inference of latent concepts when pretraining data has long-range coherence, proven for mixture-of-HMM distributions and replicated on the synthetic GINC dataset.
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Learning to Ask: When LLM Agents Meet Unclear Instruction
Introduces NoisyToolBench benchmark and Ask-when-Needed framework to improve LLM tool-use performance when user instructions are unclear or incomplete.
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Uncertainty-Aware Clarification in LLM Agents with Information Gain
The paper introduces an Information Gain Reward to train clarification behavior in LLM agents, reporting a 3.7% success rate gain over no-clarification baselines in τ-Bench evaluations across five models with minimal added steps.
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Why Build an Assistant in Minecraft?
A rationale is presented for developing an assistant in Minecraft to advance natural language understanding and dialogue learning.