SOTAVerified

Intent Detection

Intent Detection is a task of determining the underlying purpose or goal behind a user's search query given a context. The task plays a significant role in search and recommendations. A traditional approach for intent detection implies using an intent detector model to classify user search query into predefined intent categories, given a context. One of the key challenges of the task implies identifying user intents for cold-start sessions, i.e., search sessions initiated by a non-logged-in or unrecognized user.

Source: Analyzing and Predicting Purchase Intent in E-commerce: Anonymous vs. Identified Customers

Papers

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Showing 1–10 of 330 papers

TitleStatusHype
Flippi: End To End GenAI Assistant for E-Commerce—0
An Interdisciplinary Review of Commonsense Reasoning and Intent Detection—0
Invocable APIs derived from NL2SQL datasets for LLM Tool-Calling Evaluation—0
Integration of Old and New Knowledge for Generalized Intent Discovery: A Consistency-driven Prototype-Prompting FrameworkCode0
Building a Few-Shot Cross-Domain Multilingual NLU Model for Customer Care—0
Exploring the Vulnerability of the Content Moderation Guardrail in Large Language Models via Intent Manipulation—0
Seeing Through Deception: Uncovering Misleading Creator Intent in Multimodal News with Vision-Language Models—0
Learning Multimodal AI Algorithms for Amplifying Limited User Input into High-dimensional Control SpaceCode0
Enhanced Urdu Intent Detection with Large Language Models and Prototype-Informed Predictive Pipelines—0
Improving Generalization in Intent Detection: GRPO with Reward-Based Curriculum Sampling—0
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All datasetsATISMixSNIPSMixATISSNIPSASOS.com user intentBANKING77ATIS (vi)BANKING77 10-shotBANKING77 5-shotCAISCLINC150CLINC150 10-shot

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1RoBERTa-Large + ICDAAccuracy (%)97.12—Unverified