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HARP: Hesitation-Aware Reframing in Transformer Inference Pass

Romain Storaï, Seung-won Hwang

2024-12-11

HARP: Hesitation-Aware Reframing in Transformer Inference Pass

Summary

This paper talks about HARP, a new method designed to improve how large language models (LLMs) perform by managing their computational demands during the process of generating text.

What's the problem?

When large language models generate text, some parts of the process require more computing power than others. This inconsistency can slow down the model and make it less efficient, especially when the model faces uncertainty about what to say next. Traditional methods don't effectively address this issue, leading to slower response times and less optimal performance.

What's the solution?

The authors introduce HARP, which stands for Hesitation-Aware Reframing in Transformer Inference Pass. This method works by detecting when the model is uncertain about what token (word or part of a word) to generate next. Instead of rushing through, HARP allows the model to take a moment to 'pause' and reframe its input for better clarity, similar to how humans think before making a decision. This approach is easy to implement, doesn’t require retraining the model, and can be used with any existing transformer model.

Why it matters?

This research is important because it enhances the efficiency and effectiveness of language models without requiring significant changes to their structure. By improving how models handle uncertainty, HARP can lead to faster and more accurate text generation, making these models more useful for applications like chatbots, writing assistants, and other AI-driven tools.

Abstract

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.