Reinforcement Learning with Token-level Feedback for Controllable Text Generation

Core Concepts

Proposing a novel reinforcement learning algorithm named TOLE for controllable text generation with token-level rewards, enhancing robustness and performance.

Abstract

The article introduces TOLE, a reinforcement learning algorithm for controllable text generation with token-level feedback. It addresses issues of overfitting and semantic collapse in existing methods. TOLE provides precise guidance to large language models by formulating token-level rewards based on attribute classifiers. The algorithm incorporates a "first-quantize-then-noise" paradigm to enhance robustness and can be extended to multiple constraints efficiently. Experimental results demonstrate superior performance on both single-attribute and multi-attribute control tasks.

Stats

PPLM: 8.72 GEDI: 26.80 PROMPT: 40.88 PPO: 43.13 QUARK: 47.32 TOLE: 69.36

Quotes

"Our objective is to granularize the coarse-grained feedback to provide more precise guidance for LLMs." "To tackle that, we propose a novel reinforcement learning algorithm named TOLE which formulates TOken-LEvel rewards for controllable text generation." "Our algorithm can achieve superior performance on both single-attribute and multi-attribute control tasks."

Key Insights Distilled From

Reinforcement Learning with Token-level Feedback for Controllable Text Generation

by Wendi Li,Wei... at arxiv.org 03-19-2024

https://arxiv.org/pdf/2403.11558.pdf

Deeper Inquiries

How does the "Quantization & Noise" procedure enhance the robustness of the RL algorithm?

The "Quantization & Noise" procedure plays a crucial role in enhancing the robustness of the Reinforcement Learning (RL) algorithm for controllable text generation. Here are some key ways in which this procedure contributes to improving the performance and stability of the algorithm: Stability through Quantization: Quantization divides rewards into quantiles, providing a structured approach to handling reward values. By quantizing rewards, we ensure that they fall within specific intervals, preventing extreme fluctuations that could destabilize training. Noise Injection: Injecting noise into rewards helps prevent models from overfitting to specific patterns or biases present in the data. The noise disrupts fixed scoring patterns, promoting diversity in generated text and preventing models from becoming overly deterministic. Generalization: The combination of quantization and noise allows for better generalization by disrupting fixed reward patterns while maintaining relative order between intervals. This promotes more adaptive learning and prevents models from memorizing specific instances rather than learning underlying principles. Improved Exploration: The introduction of noise encourages exploration by introducing randomness into reward values, helping models explore different strategies during training. This leads to more diverse outputs and can help avoid getting stuck in local optima during optimization. In summary, "Quantization & Noise" enhances robustness by providing structure through quantization while introducing variability through noise injection, leading to improved stability, generalizability, exploration capabilities, and ultimately better performance of the RL algorithm.

How does token-level feedback compare to sentence-level feedback in terms of convergence speed and effectiveness?

Token-level feedback offers several advantages over sentence-level feedback when it comes to controllable text generation tasks: Convergence Speed: Token-level feedback typically results in faster convergence compared to sentence-level feedback. With token-level rewards guiding each individual action taken by the model during generation, it provides more precise guidance at every step. This precision allows for quicker adjustments based on immediate feedback received after generating each token. Effectiveness: Token-level feedback is more effective at providing detailed guidance on how each token contributes towards satisfying desired attributes or constraints. It enables finer-grained control over attribute fulfillment within sentences as opposed to coarse-grained signals provided by sentence-level feedback. Granularity: - Token level-feedback captures nuances within sentences that may be missed with sentence-level approaches - It allows for targeted adjustments at a granular level leading to higher quality outputs In contrast, sentence-based methods provide broader strokes but may require additional iterations due to their less precise nature Overall, token- level feedbac k is preferred f or its ability t o offer quick an d accurate guidan ce throughout th e generatio n proce ss

What are implications removing “weigher” multi-attribute combination terms performance?

When removing “weigher” from multi-attribute combinations, the following implications can be observed: 1.- Loss Precision: Without weigher balancing multiple scorers' contributions, there might be an imbalance where certain attributes receive disproportionate attention compared others 2.- Reduced Control: Ablating weigher could lead inconsistent attribute control across different tokens or parts sentences resulting suboptimal overall performance 3.- Lack Clarity: Weighers play critical role ensuring clear guidance LLMs regarding importance various attributes; without them , there may confusion conflicting signals causing decreased accuracy fulfilling all constraints simultaneously 4.- Performance Degradation: Removing weigher likely result decrease overall model performanc e especially scenarios involving multiple competing objectives requiring careful balance trade-offs