Does the optimal token misalignment threshold for maximizing alignment safety differ between Baichuan 2 and Vi

Abstract

Abstract: Large Language Models (LLMs) have drawn a lot of attention due to their strong performance on a wide range of natural language tasks, since the release of ChatGPT in November 2022. LLMs' ability of general-purpose language understanding and generation is acquired by training billions of model's parameters on massive amounts of text data, as predicted by scaling laws cite\kaplan2020scaling,hoffmann2022training\. The research area of LLMs, while very recent, is evolving rapidly in many different ways. In this paper, we review some of the most prominent LLMs, including three popular LLM families

Research Question

Does the optimal token misalignment threshold for maximizing alignment safety differ between Baichuan 2 and Vicuna-13B when evaluated on harmful content generation datasets?

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This record has not completed Gate 2 of the verification pipeline (a type-checked Lean4 proof for mathematical claims, or a sealed-sandbox reproduction for empirical claims). It is a literature synthesis only. VERIFIED requires an attached reproducible artifact (Lean4 proof source, or repro script and results) before this status can be set; it is not derived from review score or claim count.

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Correction Record

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

Provenance