TACO-RL: Task Aware Prompt Compression Optimization with Reinforcement Learning (2409.13035v3)

Abstract: The increasing prevalence of LLMs such as GPT-4 in various applications has led to a surge in the size of prompts required for optimal performance, leading to challenges in computational efficiency. Prompt compression aims to reduce the inference cost by minimizing input tokens without compromising on the task performance. However, existing prompt compression techniques either rely on sub-optimal metrics such as information entropy or model it as a task-agnostic token classification problem that fails to capture task-specific information. To address these issues, we propose a novel and efficient reinforcement learning (RL) based task-aware prompt compression method. To ensure low latency requirements, we leverage existing Transformer encoder-based token classification model while guiding the learning process with task-specific reward signals using lightweight REINFORCE algorithm. We evaluate the performance of our method on three diverse and challenging tasks including text summarization, question answering and code summarization. We demonstrate that our RL-guided compression method improves the task performance by 8% - 189% across these three scenarios over state-of-the-art compression techniques while satisfying the same compression rate and latency requirements.

Task-Aware Prompt Compression Optimization with Reinforcement Learning: A Technical Overview

In the paper titled "TACO-RL: Task-Aware Prompt Compression Optimization with Reinforcement Learning" by Shivam Shandilya et al., a novel reinforcement learning-based method is introduced for optimizing prompt compression in LLMs. Given the increasing utilization of LLMs such as GPT-4 in various applications, the length of prompts has become a significant concern due to its impact on computational efficiency and inference latency. The research targets the prompt length reduction task without compromising performance, addressing the limitations in current compression methodologies that either rely on sub-optimal heuristics or lack task-specific considerations.

Methodology and Core Contributions

The proposed method, TACO-RL, integrates reinforcement learning (RL) into the prompt compression framework effectively. Significant aspects of the methodology include:

1. Task-Specific Reward Signals: The core of TACO-RL lies in its ability to use task-specific reward signals to guide the compression process, ensuring the preserved content remains relevant to the task at hand. Rewards are computed based on the output divergence between compressed and original prompts generated by the LLM, employing metrics such as BLEU for summarization and F1 score for question-answering.
2. Reinforcement Learning Framework: The framework employs a Transformer encoder-based token classification model, fine-tuned using on-policy RL via the REINFORCE algorithm. This method leverages both bidirectional context representations and task-aware signals, optimizing the model's capability to decide which tokens to retain.
3. Compression Flexibility and Latency Control: The paper introduces a compression flexibility controller c and a tolerance threshold L to finely balance the desired compression rate and performance. This allows TACO-RL to adaptively compress prompts to meet the latency and cost constraints effectively.
4. Evaluation across Diverse Tasks: The performance of TACO-RL is empirically evaluated on three distinct tasks—text summarization, question answering, and code summarization—demonstrating its superior effectiveness compared to state-of-the-art (SoTA) techniques. Results indicate substantial improvements in task performance metrics, ranging from 8% to 260%, while maintaining competitive compression rates and latency requirements.

Empirical Results and Analysis

Text Summarization

Using the MeetingBank dataset, TACO-RL achieved impressive improvements in BLEU and ROUGE metrics across various compression ratios. For instance, at a 6x compression rate, the BLEU score increased by 45%, and ROUGE metrics also exhibited substantial enhancements compared to baseline models.

Question Answering

On the SQuAD 2.0 dataset, TACO-RL demonstrated a consistent uptrend in F1 and Exact Match (EM) scores across different compression settings. Notably, it surpassed the baseline by 11-63% in F1 scores and 22-63% in EM scores at corresponding compression rates, proving its efficacy in retaining critical context relevant to questions.

Code Summarization

In experiments on the CodeSearchNet dataset, TACO-RL showed noteworthy gains in BLEU scores when summarizing Python code. The paper found that TACO-RL could outperform the original prompt performance, highlighting its ability to distill essential information even in highly compressed prompts.

Theoretical and Practical Implications

The research contributes significantly to the field of prompt optimization in natural language processing by presenting a method that integrates task-aware learning signals with efficient RL frameworks. The theoretical implications of this work lie in the demonstrated potential of on-policy RL algorithms, specifically REINFORCE, for dynamically adjusting model policies based on task-specific objectives. This contrasts with existing approaches that often utilize static or one-size-fits-all heuristics.

Practically, the proposed method addresses real-world challenges of reducing computational overhead while ensuring high-quality outputs from LLMs. This is particularly relevant for applications requiring real-time processing, such as interactive question-answering systems and live summarization tools. The efficiency gains achieved with TACO-RL translate to tangible benefits in deploying LLMs in resource-constrained environments.

Future Directions

Building on this foundation, future research may explore:

• Broader Task Applications: Extending the method to more diverse NLP tasks, including those involving multimodal data inputs, can further validate and improve the robustness of TACO-RL.
• Enhanced Reward Mechanisms: Developing more sophisticated and hybrid reward functions that could better capture the nuances of different NLP tasks might improve fine-tuning effectiveness.
• Scalability and Deployment Efficiency: Investigating ways to make the RL-based training process more computationally efficient could make the method even more practical for large-scale use.

In conclusion, TACO-RL presents a significant advancement for prompt compression in LLMs, achieving notable improvements in task performance metrics while maintaining efficient compression rates. By leveraging task-specific reward signals and reinforcement learning, this approach sets a promising direction for future research and practical applications in computationally efficient NLP systems.