  Large language models (LLMs) for code are typically trained to align with
natural language instructions to closely follow their intentions and
requirements. However, in many practical scenarios, it becomes increasingly
challenging for these models to navigate the intricate boundary between
helpfulness and safety, especially against highly complex yet potentially
malicious instructions. In this work, we introduce INDICT: a new framework that
empowers LLMs with Internal Dialogues of Critiques for both safety and
helpfulness guidance. The internal dialogue is a dual cooperative system
between a safety-driven critic and a helpfulness-driven critic. Each critic
provides analysis against the given task and corresponding generated response,
equipped with external knowledge queried through relevant code snippets and
tools like web search and code interpreter. We engage the dual critic system in
both code generation stage as well as code execution stage, providing
preemptive and post-hoc guidance respectively to LLMs. We evaluated INDICT on 8
diverse tasks across 8 programming languages from 5 benchmarks, using LLMs from
7B to 70B parameters. We observed that our approach can provide an advanced
level of critiques of both safety and helpfulness analysis, significantly
improving the quality of output codes ($+10\%$ absolute improvements in all
models).
