Unverified50% confidenceBenchmarkExact time
The dbt Labs benchmark shows that models doing raw text-to-SQL had 32.7% accuracy in 2023, rising to 64.5% by 2026, with accuracy nearly doubling over 2.5 years.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
10/14/2026
First Seen
Valid until: 1/12/2027
Sources
Related Entities
Related Claims
UnverifiedIn the LongMemEval benchmark, BM25 accuracy improved from 78.8% to 85.2% with 5 chunks.66% similarUnverifiedThe locally run Gemma 26B achieved an accuracy of only 49.2% across 453 scored predictions, while its paired always-predict-up baseline had an accuracy of 53.4%.63% similarUnverifiedIn benchmarks, at a scale of 50,000 vectors, sqlite-vec takes approximately 84ms/query and sqlite-vector approximately 78ms/query; at 150,000 vectors, the figures are 241ms and 231ms respectively, with sqlite-vector generally querying faster.63% similarUnverified在GPT-4发布初期，主流模型在GPQA上的准确率约为35-40%，人类领域专家基准线约为65%，随机猜测为25%63% similarUnverifiedThe study found that GPT-4's accuracy on the prime number identification task plummeted from 97.6% in March 2023 to 2.4% in June 2023.61% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1025052API
curl https://kongchang.com/api/v1/knowledge/claims/1025052MCP
get_claim(id=1025052)