fudge

**FUDGE (Future Discriminators for Generation)** is a controllable text generation method that uses a **learned discriminator** to predict whether a particular **continuation** of text will satisfy a desired constraint or attribute in the **future**. Unlike PPLM which uses gradients to modify hidden states, FUDGE directly adjusts token probabilities at each generation step. **How FUDGE Works** - **Base Language Model**: A pretrained LM generates candidate next tokens as usual. - **Future Discriminator**: A separately trained classifier takes a **partial sequence** and predicts the probability that the **completed sequence** will have the desired attribute (e.g., ending with a certain word, being about a specific topic, having a particular format). - **Probability Adjustment**: At each step, token probabilities from the base LM are **multiplied** by the discriminator's predictions, boosting tokens that are likely to lead toward compliant completions. - **Decoding**: Standard sampling or beam search is applied to the adjusted distribution. **Key Advantages** - **Forward-Looking**: Unlike methods that only condition on past context, FUDGE's discriminator is trained to predict whether **future** text will satisfy constraints — enabling better planning. - **Lightweight**: The discriminator is small and fast, adding minimal overhead to generation. - **Flexible Constraints**: Can enforce hard constraints like "must end with word X" or soft attributes like "should be formal." - **No LM Modification**: The base language model remains unchanged. **Comparison with Other Methods** - **PPLM**: Uses gradients on hidden states — slower and less stable. - **FUDGE**: Uses a learned discriminator on surface text — faster and more targeted. - **GeDi**: Similar discriminator-based approach but guides generation using contrastive class probabilities. **Limitations** - Requires training a separate discriminator for each desired attribute. - The discriminator must generalize to unseen partial sequences, which can be challenging. FUDGE demonstrated that **future-aware discriminators** provide an effective and efficient mechanism for constrained text generation.