Fixing FOLIO and MALLS: Verified Annotations and an LLM-assisted Framework to Focus Human Relabeling
arXiv:2606.02837v1 Announce Type: new Abstract: Accurate translation from Natural Language to First-Order Logic (NL-to-FOL) underpins neurosymbolic AI systems and Natural Language Inference (NLI), making the quality of NL-to-FOL benchmarks essential -- yet these datasets have never been rigorously audited. Our first contribution is to present a systematic human inspection of the validation split of \textsf{FOLIO} and a subset of \textsf{MALLS} test instances, finding that approximately 39% and 36% of entries, respectively, contain incorrect FOL formalizations (i.e., ground truth labels), with
The increasing reliance on neurosymbolic AI systems and Natural Language Inference (NLI) highlights the critical need for accurate foundational datasets, making audits essential now.
This finding underscores significant quality issues in foundational NL-to-FOL benchmarks, impacting the reliability and development trajectory of core AI technologies.
The understanding of benchmark fidelity for neurosymbolic AI systems evolves, requiring more rigorous validation of datasets fundamental to AI development.
- · AI researchers focused on data quality
- · Companies offering data annotation and verification services
- · Developers of robust NL-to-FOL systems
- · AI projects relying on unverified public benchmarks
- · Developers neglecting data quality audits
- · Datasets with high error rates
Increased scrutiny and demand for verified annotations in AI datasets.
A shift towards more human-in-the-loop and LLM-assisted verification processes for AI benchmarks.
Potentially slower, but more reliable, progress in neurosymbolic AI and Natural Language Inference due to improved data foundations.
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Read at arXiv cs.CL