HARP: Hesitation-Aware Reframing in Transformer Inference Pass
Authors: name: Romain Storaï, name: Seung-won Hwang

This paper aims to improve the performance of large language models 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
thoroughly 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 offers a practical solution for
enhancing the performance of Transformer-based language models with minimal
computational impact.