Fact-Chaining Accuracy of Llama-3-8B-128K vs. Qwen-8B and Mistral-8B on BABILong Across Context Lengths

Abstract

Abstract: This report synthesises findings from 15 peer-reviewed papers addressing the following research question: How does the fact-chaining accuracy of Llama-3-8B-128K compare to Qwen-8B and Mistral-8B on the BABILong benchmark when context length increases from 32K to 128K. In recent years, the input context sizes of large language models (LLMs) have increased dramatically. However, existing evaluation methods have not kept pace, failing to comprehensively assess the efficiency of models in handling long contexts. 0 claims were extracted from source literature; 0 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 4.5/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How does the fact-chaining accuracy of Llama-3-8B-128K compare to Qwen-8B and Mistral-8B on the BABILong benchmark when context length increases from 32K to 128K?

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