What question did this study set out to answer?

The aim is to efficiently identify helpful reviews in e-commerce to support consumer decision-making and reduce information overload.

January 18, 2026

Instruction‐Tuned Large Language Models for Review Helpfulness Prediction: An Efficient Fine‐Tuning Framework for E‐Commerce Review Understanding

Key Points

The aim is to efficiently identify helpful reviews in e-commerce to support consumer decision-making and reduce information overload.
Developed ELAS-RHP, an instruction-tuned framework for review helpfulness prediction.
Utilized prompt-completion pairs and quantized low-rank adapters to fine-tune the LLaMA 3 model.
Implemented a few-shot learning strategy for effective task adaptation with minimal supervision.
ELAS-RHP consistently outperforms existing baseline models in review helpfulness prediction.
Empirical evaluations conducted using datasets from Yelp and Amazon demonstrate significant improvements.

Abstract

ABSTRACT With the exponential growth of online reviews on e‐commerce platforms, efficiently identifying helpful reviews has become increasingly critical for supporting consumer decision‐making and mitigating information overload. The task of Review Helpfulness Prediction (RHP) aims to address this challenge by automatically filtering high‐quality and reliable content from massive volumes of user‐generated reviews. While earlier studies have explored this task through both feature‐based machine learning and deep learning models, these approaches often struggle to capture the complex linguistic nuances and contextual dependencies inherent in review texts. Although Transformer‐based models such as BERT have improved contextual representation learning, they rely on large‐scale labelled data and require extensive task‐specific fine‐tuning, which limits their adaptability and scalability in dynamic application settings. To overcome these limitations, we propose ELAS‐RHP, a novel instruction‐tuned framework grounded in Large Language Models (LLMs) that explicitly aligns model behaviour with the characteristics of the RHP task. Specifically, we reformulate review data into prompt–completion pairs and apply Quantized Low‐Rank Adapters (QLoRA) to efficiently fine‐tune the LLaMA 3 model with reduced computational overhead. By incorporating a few‐shot learning strategy, ELAS‐RHP enables effective task adaptation under minimal supervision and constrained resources. Empirical evaluations conducted on real‐world datasets from Yelp and Amazon demonstrate that our framework consistently outperforms existing baselines across multiple evaluation scenarios. This study provides one of the first empirical investigations into instruction‐tuned LLMs for RHP and presents a scalable, efficient and context‐aware solution for enhancing review‐based information processing in e‐commerce environments.

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Instruction‐Tuned Large Language Models for Review Helpfulness Prediction: An Efficient Fine‐Tuning Framework for E‐Commerce Review Understanding

Key Points

Abstract

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