Adaptable Logical Control for Large Language Models (2406.13892v2)

Abstract: Despite the success of LLMs on various tasks following human instructions, controlling model generation at inference time poses a persistent challenge. In this paper, we introduce Ctrl-G, an adaptable framework that facilitates tractable and flexible control of LLM generation to reliably follow logical constraints. Ctrl-G combines any production-ready LLM with a Hidden Markov Model, enabling LLM outputs to adhere to logical constraints represented as deterministic finite automata. We show that Ctrl-G, when applied to a TULU2-7B model, outperforms GPT3.5 and GPT4 on the task of interactive text editing: specifically, for the task of generating text insertions/continuations following logical constraints, Ctrl-G achieves over 30% higher satisfaction rate in human evaluation compared to GPT4. When applied to medium-size LLMs (e.g., GPT2-large), Ctrl-G also beats its counterparts for constrained generation by large margins on standard benchmarks. Additionally, as a proof-of-concept study, we experiment Ctrl-G on the Grade School Math benchmark to assist LLM reasoning, foreshadowing the application of Ctrl-G, as well as other constrained generation approaches, beyond traditional language generation tasks.

Adaptable Logical Control for LLMs

The paper "Adaptable Logical Control for LLMs" by Zhang et al. addresses a significant challenge in the field of AI language generation: the difficulty of controlling the outputs of LLMs to adhere to specified logical constraints during inference. LLMs like GPT-3.5 and GPT-4, while highly capable in generating coherent and contextually appropriate text, struggle with strictly following constraints imposed by downstream tasks, which is a limitation in applications requiring fine-grained control, such as document revision and toxicity avoidance.

Ctrl-G Framework

The authors propose Ctrl-G, an adaptable framework combining LLMs with a Hidden Markov Model (HMM) to enforce logical constraints represented as deterministic finite automata (DFAs). The architecture consists of three key components:

1. Distillation: The LLM is approximated by an HMM through a distillation process, where the HMM is trained on samples from the LLM.
2. Constraint Specification: Logical constraints are encoded as DFAs.
3. Inference: The Ctrl-G uses the HMM, conditioned on the DFA-specified constraints, to guide the LLM's autoregressive generation process.

The HMM provides a structured probabilistic model that facilitates efficient computation of the conditional probabilities, ensuring constraints are satisfied without further training regardless of the changing logical constraints.

Technical Contributions and Results

Constraint Satisfaction

The major technical advancement presented in Ctrl-G lies in its ability to incorporate any logical constraints defined as DFAs. This approach guarantees that these constraints will be met during generation, a level of reliability that other methods like GeDi, FUDGE, and NeuroLogic decoders fail to consistently achieve.

Performance Evaluation

The paper provides robust empirical evidence of the effectiveness of Ctrl-G:

• Interactive Text Editing: When evaluated on the task of text continuation and insertion with logical constraints, Ctrl-G applied to the TULU2-7B model surpasses GPT3.5 and GPT4. Specifically, human evaluations show that Ctrl-G achieves over a 30% higher satisfaction rate.
• Commonsense Generation: On the CommonGen benchmark, Ctrl-G demonstrates a 100% constraint satisfaction rate with higher BLEU, ROUGE, CIDEr, and SPICE scores compared to other constrained generation frameworks.
• Text Infilling: It also outperforms supervised models on the task of text infilling, particularly as the masking ratio increases.

Theoretical and Practical Implications

Technically, the time complexity of sampling from Ctrl-G is linear in the sequence length, demonstrating feasible scalability. The forward algorithm employed effectively manages the conditional probabilities, even as constraints GROW more complex.

From a practical perspective, Ctrl-G extends the usability of LLMs in scenarios requiring adherence to specific formats or rules, such as legal document drafting, automated code completion, and educational content generation. The adaptability of Ctrl-G to new constraints without retraining positions it as a future-ready solution in AI-driven content creation domains.

Future Directions

The paper hints at intriguing future research trajectories. One possibility is leveraging the framework to refine LLMs' reasoning capabilities by aligning the generation process with logically defined steps encoded as DFAs. This could notably enhance performance on benchmarks like the Grade School Math benchmark, where LLMs' reasoning processes often falter.

Another potential direction includes extending Ctrl-G to a wider array of NLP tasks, such as sentiment control and domain-specific text generation, which could provide nuanced control over the text characteristics, further broadening the practical applications of LLMs.

In conclusion, Ctrl-G emerges as a significant contribution to LLM control methodologies. It amalgamates probabilistic reasoning with formal logical specifications, offering a reliable and versatile solution to a longstanding challenge in AI language generation.