Advances in Intent Understanding and Reasoning

The field of natural language processing is moving towards incorporating more sophisticated reasoning and intent understanding capabilities into language models. Recent research has focused on developing methods that can effectively capture user intent, particularly in complex and dynamic environments. This includes the use of large language models (LLMs) to improve intent recognition, multimodal intent understanding, and the integration of reasoning capabilities into text embedding processes.

Noteworthy papers in this area include Exploring Reasoning-Infused Text Embedding with Large Language Models for Zero-Shot Dense Retrieval, which proposes a novel approach to integrating logical reasoning into the text embedding process. LLM-Guided Semantic Relational Reasoning for Multimodal Intent Recognition is also noteworthy, as it harnesses the knowledge of LLMs to establish semantic foundations for relational reasoning.

These advancements have significant implications for a range of applications, including conversational AI, search engines, and fake news detection. As the field continues to evolve, we can expect to see even more innovative approaches to intent understanding and reasoning emerge.

Sources

Exploring Reasoning-Infused Text Embedding with Large Language Models for Zero-Shot Dense Retrieval

LLM-Guided Semantic Relational Reasoning for Multimodal Intent Recognition

Bridging Thoughts and Words: Graph-Based Intent-Semantic Joint Learning for Fake News Detection

FaMA: LLM-Empowered Agentic Assistant for Consumer-to-Consumer Marketplace

RECAP: REwriting Conversations for Intent Understanding in Agentic Planning

Do Large Language Models Need Intent? Revisiting Response Generation Strategies for Service Assistant

Few-Shot Query Intent Detection via Relation-Aware Prompt Learning

Unified Interaction Foundational Model (UIFM) for Predicting Complex User and System Behavior

Enhancing Low-Altitude Airspace Security: MLLM-Enabled UAV Intent Recognition

Reasoning-enhanced Query Understanding through Decomposition and Interpretation

CogGuide: Human-Like Guidance for Zero-Shot Omni-Modal Reasoning

On the Same Wavelength? Evaluating Pragmatic Reasoning in Language Models across Broad Concepts

NOWJ@COLIEE 2025: A Multi-stage Framework Integrating Embedding Models and Large Language Models for Legal Retrieval and Entailment