Benchmarking graph construction by large language models for coherence-driven inference
Fuente:
arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915452320481280 |
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| author | Huntsman, Steve Thomas, Jewell |
| author_facet | Huntsman, Steve Thomas, Jewell |
| contents | We devise an algorithm to generate propositions that objectively instantiate graphs supporting coherence-driven inference. We also benchmark the ability of large language models (LLMs) to reconstruct coherence graphs from (a simple transformation of) propositions expressed in natural language, with promising results from a single prompt to reasoning-optimized LLMs. For example, o1/3/4-mini achieve perfect reconstruction half of the time on sparse graphs. Coherence-driven inference on consistency evaluations by LLMs may advance machine cognition capabilities. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_13953 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Benchmarking graph construction by large language models for coherence-driven inference Huntsman, Steve Thomas, Jewell Artificial Intelligence We devise an algorithm to generate propositions that objectively instantiate graphs supporting coherence-driven inference. We also benchmark the ability of large language models (LLMs) to reconstruct coherence graphs from (a simple transformation of) propositions expressed in natural language, with promising results from a single prompt to reasoning-optimized LLMs. For example, o1/3/4-mini achieve perfect reconstruction half of the time on sparse graphs. Coherence-driven inference on consistency evaluations by LLMs may advance machine cognition capabilities. |
| title | Benchmarking graph construction by large language models for coherence-driven inference |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2502.13953 |