Unverified50% confidenceFactExact time
The final dictionary is evaluated by a more capable LLM and calibrated against human expert annotations via Cohen's κ.
1
Sources
50%
Confidence
Long-term
Relevance
11/4/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedCommon implementation approaches for LLM semantic-level assertions include rule-based checkers, embedding vector cosine similarity thresholds (e.g., 0.85), LLM-as-judge scoring, and NLI model-based factual consistency verification.68% similarUnverifiedThe researchers recommend combining the deterministic checker with the LLM verifier: use the deterministic checker for high-confidence certification, use the LLM verifier to broaden recall, and then manually or rule-based review the claims accepted by the LLM.68% similarVerifiedFor complex Skills that require reviewing complete trajectories, LLM as a Judge can be introduced along with a rubric to determine pass or fail.67% similarUnverifiedLLMs can serve as auxiliary tools to quickly screen obvious errors and pre-annotate candidate spans, but final judgment still requires human review, especially in scenarios with high precision requirements.66% similarUnverifiedTo make LLM-as-a-judge results credible, you need: write the evaluation criteria as decidable rules with positive and negative examples; force the output into a structured format (such as a JSON binary classification or a 1–5 score); swap order or apply debiasing to address position bias and length bias; and calibrate the judge's agreement with humans using a small batch of human-labeled samples (such as Cohen's kappa).66% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1134116API
curl https://kongchang.com/api/v1/knowledge/claims/1134116MCP
get_claim(id=1134116)