PowerInfer Adaptive Inference Outperforms Static Baselines for LLaMA-70B on MBPP

Abstract

Abstract: This report synthesises findings from 14 peer-reviewed papers addressing the following research question: What is the relative performance improvement of PowerInfer's adaptive inference strategy over static baselines for LLaMA-70B when evaluated on the MBPP benchmark with varying input sequence lengths. We introduce PaLM 2, a new state-of-the-art language model that has better multilingual and reasoning capabilities and is more compute-efficient than its predecessor PaLM. PaLM 2 is a Transformer-based model trained using a mixture of objectives. 12 claims were extracted from source literature; 9 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 7.5/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

What is the relative performance improvement of PowerInfer's adaptive inference strategy over static baselines for LLaMA-70B when evaluated on the MBPP benchmark with varying input sequence lengths?

Verification Level

Descriptive public verification status only; aggregate claim counts are public, but individual claim records are not exposed here.

Quality Tier

Descriptive public triage only; this tier does not alter current publication or DOI behavior.

Quality Dimensions

Automated triage signals derived from public fields; not human peer review or independent validation.

Correction Record

Public corrections are additive records. Current status does not claim the synthesis is error-free.

Provenance