RLTasksby ulam.ai
Models / Microsoft

Microsoft / MODEL EVIDENCE

Phi-3.5-mini-instruct

1 benchmarks · 6 observations · 1 sources

Scores at a glance

1 benchmarks · large dot: Phi-3.5-mini-instruct; small dots: closest models where reported

LongMemEval ↗31.2%
0.831.2

Phi-3.5-mini-instruct · LongMemEval · LongMemEval S · full history direct · QA accuracy · additional-model comparison ↗

5 more reported results

35.2% · Phi-3.5-mini-instruct · LongMemEval · LongMemEval S · full history CoN · QA accuracy · additional-model comparison · LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memory ↗

55% · Phi-3.5-mini-instruct · LongMemEval · LongMemEval S · key=value · QA accuracy · additional-model comparison · LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memory ↗

57% · Phi-3.5-mini-instruct · LongMemEval · LongMemEval S · fact-expanded keys · QA accuracy · additional-model comparison · LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memory ↗

46.2% · Phi-3.5-mini-instruct · LongMemEval · LongMemEval M · key=value · QA accuracy · additional-model comparison · LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memory ↗

46.8% · Phi-3.5-mini-instruct · LongMemEval · LongMemEval M · fact-expanded keys · QA accuracy · additional-model comparison · LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memory ↗

Closest models by score

Similar positions on shared benchmark leaderboards. Smaller gaps are closer.

50.0 percentile-point gap

1 of 1 shared benchmark comparisons

How similarity is measured

Mean absolute gap in percentile positions on shared benchmark settings; lower is closer. At least 60% overlap is required, with a minimum of three when available. Independent evaluations are used when there are at least three; otherwise published report comparisons are used. Repeated model observations within one setting are averaged before ranking. Writing-style traits and composite indices are excluded. This describes similarity, not an overall quality ranking.

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Sources

Every plotted observation links to its evaluation. Reported configurations, benchmark versions and task subsets can differ; similarity describes shared scores, not training lift.

  1. LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memory — published result table ↗

    Wu, Di, Wang, Hongwei, Yu, Wenhao, Zhang, Yuwei, Chang, Kai-Wei, Yu, Dong · 2024

Names used in source tables

Phi-3.5-mini-instruct