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Knowledge Boundary Discovery for Large Language Models

arXiv:2603.21022v1 Announce Type: new Abstract: We propose Knowledge Boundary Discovery (KBD), a reinforcement learning based framework to explore the knowledge boundaries of the Large Language Models (LLMs). We define the knowledge boundary by automatically generating two types of questions: (i) those the LLM can confidently answer (within-knowledge boundary) and (ii) those it cannot (beyond-knowledge boundary). Iteratively exploring and exploiting the LLM's responses to find its knowledge boundaries is challenging because of the hallucination phenomenon. To find the knowledge boundaries of an LLM, the agent interacts with the LLM under the modeling of exploring a partially observable environment. The agent generates a progressive question as the action, adopts an entropy reduction as the reward, receives the LLM's response as the observation and updates its belief states. We demonstrate that the KBD detects knowledge boundaries of LLMs by automatically finding a set of non-trivial a

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Ziquan Wang, Zhongqi Lu · · 1 min read · 10 views
#cs.AI #cs.CL #cs.LG

arXiv:2603.21022v1 Announce Type: new Abstract: We propose Knowledge Boundary Discovery (KBD), a reinforcement learning based framework to explore the knowledge boundaries of the Large Language Models (LLMs). We define the knowledge boundary by automatically generating two types of questions: (i) those the LLM can confidently answer (within-knowledge boundary) and (ii) those it cannot (beyond-knowledge boundary). Iteratively exploring and exploiting the LLM's responses to find its knowledge boundaries is challenging because of the hallucination phenomenon. To find the knowledge boundaries of an LLM, the agent interacts with the LLM under the modeling of exploring a partially observable environment. The agent generates a progressive question as the action, adopts an entropy reduction as the reward, receives the LLM's response as the observation and updates its belief states. We demonstrate that the KBD detects knowledge boundaries of LLMs by automatically finding a set of non-trivial answerable and unanswerable questions. We validate the KBD by comparing its generated knowledge boundaries with manually crafted LLM benchmark datasets. Experiments show that our KBD-generated question set is comparable to the human-generated datasets. Our approach paves a new way to evaluate LLMs.

Executive Summary

This article presents Knowledge Boundary Discovery (KBD), a reinforcement learning-based framework to explore the knowledge boundaries of Large Language Models (LLMs). By iteratively generating questions and analyzing LLM responses, KBD identifies both answerable and unanswerable questions, providing a novel approach to evaluating LLMs. Experiments demonstrate that KBD-generated questions are comparable to human-generated datasets. The framework's potential to improve LLM evaluation and understanding is significant.

Key Points

  • ▸ KBD proposes a reinforcement learning-based framework for exploring LLM knowledge boundaries
  • ▸ The framework iteratively generates questions and analyzes LLM responses to identify answerable and unanswerable questions
  • ▸ Experiments demonstrate that KBD-generated questions are comparable to human-generated datasets

Merits

Strength in Evaluation

KBD provides a systematic and automated method for evaluating LLMs, reducing reliance on manual benchmark datasets and enabling more frequent evaluation and improvement.

Demerits

Hallucination Phenomenon Limitation

The article acknowledges the hallucination phenomenon, where LLMs may produce incorrect or irrelevant responses, which could impact the accuracy and reliability of KBD-generated questions.

Expert Commentary

The introduction of KBD marks a significant advancement in the field of LLM evaluation. By leveraging reinforcement learning and automatic question generation, researchers can more effectively identify knowledge boundaries and areas of uncertainty. While the hallucination phenomenon poses a challenge, it also underscores the need for more sophisticated evaluation metrics and methodologies. KBD's potential to improve LLM training, deployment, and regulation makes it a crucial contribution to the field.

Recommendations

  • ✓ Future research should explore the integration of KBD with other evaluation frameworks and methodologies to create a more comprehensive understanding of LLM capabilities and limitations.
  • ✓ The development of more robust and accurate LLM evaluation metrics is essential to ensure the reliable and transparent use of these models in various applications.

Sources

Original: arXiv - cs.AI

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