Key Points
1. The paper introduces Ctrl-G, an adaptable framework that combines a production-ready LLM with a Hidden Markov Model to enable tractable and flexible control of LLM generation to reliably follow logical constraints.
2. Ctrl-G represents logical constraints as deterministic finite automata, which can be efficiently constructed for various applications.
3. Ctrl-G computes the conditional probability phmm(α|xt,x<t) to guide the LLM generation towards satisfying the given constraint α.
4. Compared to prior work, Ctrl-G guarantees that the logical constraints will be satisfied, requires no further training when the constraints change, and can handle a wide range of constraints specified as DFAs.
5. Ctrl-G, when applied to the TULU2-7B model, outperforms GPT3.5 and GPT4 on the task of interactive text editing, achieving over 30% higher satisfaction rate in human evaluation.
6. Ctrl-G also beats other constrained generation approaches by large margins on standard benchmarks like CommonGen and text infilling.
7. As a proof-of-concept, the paper experiments with Ctrl-G on the Grade School Math benchmark to assist LLM reasoning, suggesting the potential application of Ctrl-G beyond traditional language generation tasks.
8. Ctrl-G subsumes pure logical reasoning approaches, as it performs probabilistic reasoning to estimate how likely each next token would eventually lead to the constraint being satisfied.
9. The paper provides efficient algorithms for constructing compact DFAs representing various logical constraints and computing the desired marginal probabilities efficiently.
Summary
The paper introduces a framework called Ctrl-G that aims to address the challenge of controlling the generation of large language models (LLMs) to reliably follow logical constraints. Ctrl-G combines an LLM with a Hidden Markov Model (HMM) to enable the LLM's outputs to adhere to logical constraints represented as deterministic finite automata (DFAs).
When applied to a TULU2-7B model, the authors show that Ctrl-G 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. The authors attribute this improvement to Ctrl-G's ability to reliably follow logical constraints, while GPT3.5 and GPT4 struggle with this.
Model Size Demonstration
The paper also demonstrates that when applied to medium-size language models like GPT2-large, Ctrl-G beats other constrained generation approaches by large margins on standard benchmarks. This highlights Ctrl-G's strong generalization capabilities beyond just large LLMs.
Proof-of-Concept Experiment
As a proof-of-concept, the authors experiment with Ctrl-G on the Grade School Math benchmark, suggesting potential applications beyond traditional language generation tasks. Specifically, they show that Ctrl-G can be used to assist LLM reasoning by encoding information as keyphrase constraints. This leads to a 3.4% improvement in accuracy on a subset of the Grade School Math test examples where the LLM failed to use all the numbers provided in the problem statement.
Overall, the key contributions of this work are: 1) the introduction of the Ctrl-G framework that combines LLMs with HMMs to enable reliable constrained generation, 2) empirical results showing Ctrl-G outperforming strong baselines on interactive text editing and other constrained generation benchmarks, and 3) initial experiments demonstrating Ctrl-G's potential for improving LLM reasoning abilities beyond just language generation. The authors argue that this work opens up new avenues for fine-grained inference-time control of LLMs.
Reference: https://arxiv.org/abs/2406.13892