Adaptable Logical Control for Large Language Models

🌈 Abstract

The paper introduces Ctrl-G, a framework that enables reliable and flexible inference-time control of large language models (LLMs) to generate outputs that comply with logical constraints. Ctrl-G combines an LLM with a Hidden Markov Model (HMM) to guide the LLM's generation towards satisfying the specified constraints, which are represented as deterministic finite automata (DFAs). The authors show that Ctrl-G outperforms prominent LLMs like GPT-3.5 and GPT-4 on tasks like interactive text editing, commonsense generation, and text infilling, while maintaining high generation quality. They also explore the potential of using Ctrl-G to improve LLM reasoning abilities on the Grade School Math benchmark.

🙋 Q&A

[01] Ctrl-G Framework

1. What are the three main steps of the Ctrl-G pipeline? The Ctrl-G pipeline consists of three steps:

• Distillation: Distilling an HMM as a white-box approximation of the target LLM.
• Constraint specification: Constructing a DFA to represent the desired logical constraint.
• Inference: Conditioning the HMM on the DFA-specified constraint to guide the LLM's generation towards satisfying the constraint.

2. How does Ctrl-G guarantee that the logical constraints will be satisfied? Ctrl-G uses the HMM to compute the marginal probability of the LLM's generation satisfying the logical constraint represented by the DFA. This allows Ctrl-G to guide the LLM's generation towards satisfying the constraint, unlike previous approaches that do not provide such guarantees.

3. What are the key advantages of Ctrl-G compared to its counterparts? The key advantages of Ctrl-G are:

• The desired logical constraints are guaranteed to be satisfied.
• Once the HMM is distilled, no further training is required, regardless of how the constraints change.
• Ctrl-G can handle any constraints specified as DFAs, which can be easily constructed for various applications.

[02] Commonsense Generation Experiments

1. How does Ctrl-G perform on the Commonsense Generation (CommonGen) benchmark? Ctrl-G, when applied to a GPT2-large model, outperforms prior constrained generation approaches like FUDGE, NADO, NeuroLogic A*esque decoding, and GeLaTo on the CommonGen benchmark. Ctrl-G achieves 100% constraint satisfaction rate while also producing outputs with much higher BLEU, ROUGE, CIDEr, and SPICE scores.

2. How does Ctrl-G's performance scale as the number of concepts (keywords) increases? The authors construct a more challenging test set, CommonGen+, by augmenting the original CommonGen test set with additional keywords. Ctrl-G maintains a 100% constraint satisfaction rate and high generation quality across settings with varying numbers of concepts, demonstrating strong generalizability.

[03] Text Infilling Experiments

1. How does Ctrl-G perform on the text infilling benchmark? When applied to a GPT2-small model, Ctrl-G outperforms the ILM model (which is trained on the text infilling benchmark) in terms of BLEU and ROUGE scores, especially as the masking ratio increases. This highlights Ctrl-G's strong generalizability compared to supervised approaches.

2. How does Ctrl-G incorporate the information about the granularity of the masked-out parts (e.g., [WORD], [SENTENCE])? To incorporate the information about the masked-out parts, Ctrl-G constructs additional DFAs to enforce the model to generate text that satisfies the specified granularity constraints, in addition to the constraints on the unmasked parts.

[04] Interactive Text Editing Experiments

1. How does Ctrl-G perform on the task of interactive text editing compared to GPT-3.5 and GPT-4? When applied to the TULU2-7B model, Ctrl-G outperforms GPT-3.5 and GPT-4 on the task of interactive text editing, specifically for generating text insertions/continuations under various logical constraints. Ctrl-G achieves over 30% higher satisfaction rate in human evaluation compared to GPT-4.

2. How does the generation quality of Ctrl-G and the baselines change as the logical constraints become more complex? The generation quality of GPT-4 declines as the logical constraints become more complex, while Ctrl-G's generation quality stays relatively consistent across all settings, demonstrating strong generalizability.

3. How does Ctrl-G's runtime scale with the size of the DFA and the maximum sequence length? The time for generating each token from Ctrl-G scales roughly linearly with the size of the DFA, while the extra computation cost introduced by Ctrl-G (compared to the base model) stays constant, regardless of the maximum sequence length.

[05] Improving LLM Reasoning Abilities

1. How does Ctrl-G help improve the TULU2-7B model's reasoning abilities on the Grade School Math (GSM) benchmark? As a proof-of-concept, the authors use Ctrl-G to provide certain information to the TULU2-7B model's reasoning process by encoding it as keyphrase constraints. This leads to a 3.4% improvement in the model's accuracy on the GSM benchmark, compared to the model without Ctrl-G.

2. What are the potential future applications of Ctrl-G beyond traditional constrained generation tasks? The authors suggest that Ctrl-G could be used to help LLM detoxification by conditioning on the non-appearance of bad words/phrases, or to improve the reasoning abilities of LLMs on a broader scope of downstream applications beyond just language generation tasks.