Noise or Nuance: An Investigation Into Useful Information and Filtering For LLM Driven AKBC
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918139000782848 |
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| author | Clay, Alex Jiménez-Ruiz, Ernesto Madhyastha, Pranava |
| author_facet | Clay, Alex Jiménez-Ruiz, Ernesto Madhyastha, Pranava |
| contents | RAG and fine-tuning are prevalent strategies for improving the quality of LLM outputs. However, in constrained situations, such as that of the 2025 LM-KBC challenge, such techniques are restricted. In this work we investigate three facets of the triple completion task: generation, quality assurance, and LLM response parsing. Our work finds that in this constrained setting: additional information improves generation quality, LLMs can be effective at filtering poor quality triples, and the tradeoff between flexibility and consistency with LLM response parsing is setting dependent. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_08903 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Noise or Nuance: An Investigation Into Useful Information and Filtering For LLM Driven AKBC Clay, Alex Jiménez-Ruiz, Ernesto Madhyastha, Pranava Computation and Language RAG and fine-tuning are prevalent strategies for improving the quality of LLM outputs. However, in constrained situations, such as that of the 2025 LM-KBC challenge, such techniques are restricted. In this work we investigate three facets of the triple completion task: generation, quality assurance, and LLM response parsing. Our work finds that in this constrained setting: additional information improves generation quality, LLMs can be effective at filtering poor quality triples, and the tradeoff between flexibility and consistency with LLM response parsing is setting dependent. |
| title | Noise or Nuance: An Investigation Into Useful Information and Filtering For LLM Driven AKBC |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2509.08903 |