How does the Baichuan 2 model's performance in low-resource inference settings compare to Meta AI's LLaMA-3 on

Abstract

Abstract: Intervening the internal activations of large language models (LLMs) provides an effective inference-time alignment approach to mitigate undesirable behaviors, such as generating erroneous or harmful content, thereby ensuring safe and reliable applications of LLMs.However, previous methods neglect the misalignment discrepancy among varied tokens, resulting in deviant alignment direction and inflexible editing strength.To address these issues, we propose a token-aware editing (TAE) approach to fully utilize token-level alignment information in the activation space, therefore realizing superior

Research Question

How does the Baichuan 2 model's performance in low-resource inference settings compare to Meta AI's LLaMA-3 on the TrustLLM benchmark in terms of accuracy and latency trade-offs?

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