HARP: Hesitation-Aware Reframing in Transformer Inference Pass (2412.07282v2)

Abstract: This paper aims to improve the performance of LLMs by addressing the variable computational demands in inference steps, where some tokens require more computational resources than others. We present HARP, a simple modification to "off-the-shelf" Transformer forward pass. Drawing from hesitation and the framing effect in decision-making, HARP selectively applies additional computation when the model encounters uncertainty during token generation. Our method mimics human cognitive processes by pausing at difficult decision points and reframing inputs for a different perspective. Unlike other approaches, HARP is model-agnostic, training-free, and easy to implement. We evaluate our method across various downstream tasks and model sizes, demonstrating performance improvements up to +5.16%. Notably, HARP achieves these gains while maintaining inference times twice faster than beam search. Simple and yet with significant gains, HARP provides insights into the potential of adaptive computation for enhancing the performance of Transformer-based LLMs.

• The paper introduces HARP, a method that identifies computationally demanding tokens and allocates extra resources to enhance Transformer inference.
• It employs token-level uncertainty estimation using Shannon entropy with embedding dropout for adaptive input reframing during generation.
• Experimental results demonstrate up to +5.16% accuracy gains and doubled inference speed across tasks without requiring retraining.

An Expert Overview of "HARP: Hesitation-Aware Reframing in Transformer Inference Pass"

The research paper titled "HARP: Hesitation-Aware Reframing in Transformer Inference Pass" introduces a novel approach aimed at optimizing the performance of Transformer-based LLMs during inference. The authors propose a mechanism, HARP, which stands for Hesitation-Aware Reframed Forward Pass. This method identifies computationally demanding tokens during the generative process and strategically applies additional computation to address these demands, enhancing both efficiency and performance without necessitating retraining or fine-tuning.

Traditional Transformer architectures, exemplified by models like LLaMA, Mistral, and Phi, apply a uniform computational effort during inference. However, inference steps may vary in complexity, with some requiring more intensive computational resources than others. This one-size-fits-all approach can lead to inefficient use of resources and suboptimal model performance. The authors challenge this status quo by drawing insights from human decision-making processes such as hesitation and cognitive reframing. HARP operationalizes these concepts by selectively enhancing computational resources through uncertainty detection and adaptive input modification, or "reframing."

Key Methodological Contributions

HARP's operation hinges on two main mechanisms: uncertainty estimation and input reframing. During standard Transformer inference, token-level uncertainty is quantified using Shannon entropy—a robust measure indicative of prediction confidence across a vocabulary distribution. When entropy values exceed a predefined threshold, signaling model hesitation, the input sequence is algorithmically perturbed using embedding dropout to offer an alternative representation. This reframed input is then re-processed through the Transformer model, and the outcomes from both passes are combined, favoring a holistic decision-making process.

The paper establishes the superiority of HARP over methods like beam search by demonstrating significant improvements across diverse datasets and task types, including CommonSenseQA, GSM8K, and CNN/Daily Mail summarization. Remarkably, HARP achieves accuracy gains up to +5.16% while maintaining efficiency, achieving inference times up to twice as fast compared to beam search, on average.

Experimental Insights and Performance

Evaluations conducted on state-of-the-art aligned LLMs, encompassing models of varying sizes (3B to 8B parameters), confirm HARP's efficacy. The method consistently enhanced generation performance, especially in problem-solving tasks where cognitive load is predominant. Additionally, the approach proved compatible with advanced prompting techniques, such as Chain-of-Thought, yielding compounded performance benefits.

HARP’s strategic focus on token-level uncertainty allows it to pinpoint "harder" inference steps accurately, effectively prioritizing computational efforts where they are needed most. This adaptability underscores its practical value, particularly in settings constrained by computational limits.

Implications and Future Directions

The practical implications of HARP's cost-effective enhancement to Transformer inference processes are significant. By optimizing computational distribution without altering the fundamental architecture of LLMs, this method lends itself well to scenarios demanding rapid yet accurate generative capabilities. Its model-agnostic nature further broadens its utility across a wide spectrum of LLMs and application contexts.

Future trajectories for this line of research could involve expanding HARP's utility to encompass much larger models or integrating it with speculative decoding frameworks. Furthermore, empirical investigations into alternative methods of uncertainty quantification and input perturbation could yield additional optimizations while preserving computational efficiency.

In conclusion, HARP presents a compelling advance in the field of adaptive computation for LLMs, bridging insights from cognitive psychology with computational linguistics to deliver nuanced, efficient, and performance-enhancing Transformer inference capabilities.