  Large Language Models (LLMs) have demonstrated a powerful ability for text
generation. However, achieving optimal results with a given prompt or
instruction can be challenging, especially for billion-sized models.
Additionally, undesired behaviors such as toxicity or hallucinations can
manifest. While much larger models (e.g., ChatGPT) may demonstrate strength in
mitigating these issues, there is still no guarantee of complete prevention. In
this work, we propose formalizing text generation as a future-constrained
generation problem to minimize undesirable behaviors and enforce faithfulness
to instructions. The estimation of future constraint satisfaction, accomplished
using LLMs, guides the text generation process. Our extensive experiments
demonstrate the effectiveness of the proposed approach across three distinct
text generation tasks: keyword-constrained generation (Lin et al., 2020),
toxicity reduction (Gehman et al., 2020), and factual correctness in
question-answering (Gao et al., 2023).
