WenSight decision model improves e commerce rule outcomes accuracy
WenSight-Decision-1 for Direct Decisions under E-commerce Rules
Computation and Language
Summary
Making decisions in online shopping often relies on understanding complex rules written in plain language. The authors created WenSight-Decision-1, a model that directly compares possible choices against these rules using examples in six different languages. It learns by using scores as examples and performs better than its predecessor, achieving much higher accuracy on test cases. The model also works well across several different e-commerce decision-making tasks using a unified approach.
What this means in practice
- •For e-commerce platform developers: Integrate WenSight-Decision-1 to improve automated decisions by directly scoring options with natural language rules in multiple languages.
- •For multilingual customer support teams: Use WenSight-Decision-1 to assist in consistent rule-based decision making across six languages for operational tasks in e-commerce.
Authors
Mingwen Liu, Jun Sun, Sichen Wang
Abstract
E-commerce decisions require applying natural-language rules to the available case evidence. We introduce WenSight-Decision-1, a shared model that scores supplied options directly from rules and case information for six operational tasks, with examples spanning six languages. We fine-tune Qwen3.5-9B with reference options as supervision, using the same option scores for training and inference. On 1,440 synthetic operational cases reserved for final evaluation, accuracy rises from 61.60% for Qwen3.5-9B to 92.92% for WenSight-Decision-1 under identical inputs and scoring. Joint fine-tuning improves all six tasks, bringing heterogeneous operational judgments under a common decision interface.