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Benchmarking graph construction by large language models for coherence-driven inference

Created by
  • Haebom

Author

Steve Huntsman, Jewell Thomas

Outline

This paper presents an algorithm for generating propositions that objectively represent graphs supporting consistency-based reasoning. Furthermore, we benchmark the ability of large-scale language models (LLMs) to reconstruct consistency graphs from propositions expressed in natural language (simply transformed). We demonstrate promising results using a single prompt on an LLM optimized for inference. For example, o1/3/4-mini achieves perfect reconstruction in half the time for sparse graphs. Consistency-based reasoning for consistency assessment by LLMs could enhance machine cognitive capabilities.

Takeaways, Limitations

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Takeaways:
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We present a novel algorithm for generating graphs that support consistency-based inference.
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Promising results demonstrating the potential of consistency graph reconstruction using LLM
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Suggesting the possibility of improving machine cognitive abilities through consistency assessment of LLM.
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Limitations:
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The performance evaluation of the proposed algorithm and LLM is limited to sparse graphs.
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Further research is needed on generalizability to different types of graphs and propositions.
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Potential limitations in LLM performance due to the use of a single prompt
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A more in-depth analysis of the consistency assessment of LLMs is needed.
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