Cross-Lingual Deep Search Agent
Project Description
Traditional keyword-based search engines and conventional large language model (LLM) search applications suffer from critical limitations in cross-lingual scenarios, including rigid keyword matching, insufficient semantic understanding across languages, poor adaptability to low-resource languages, and failure to capture deep contextual, logical, and implicit user intent. This summer UROP project focuses on designing, building, and iteratively optimizing an end-to-end cross-lingual deep search agent system.
The project will integrate cutting-edge cross-lingual pre-trained models, retrieval-augmented generation (RAG) pipelines, multi-level semantic alignment mechanisms, and intent parsing modules to break through language barriers in global information retrieval. Different from general search tools, this agent targets deep search tasks that require logical reasoning, cross-language knowledge fusion, and multi-source information verification, including cross-lingual academic literature retrieval, multilingual technical document mining, and cross-border open-domain fact checking.
During the project, we will construct a custom cross-lingual test dataset covering high-resource languages (English, Chinese) and typical low-resource regional languages, benchmark mainstream cross-lingual retrieval and generation models, and optimize core modules including cross-language semantic embedding alignment, noise filtering of retrieved content, and adaptive intent reasoning. The final deliverable will be a functional prototype of the cross-lingual deep search agent, along with comprehensive ablation experiment results and performance analysis reports, which can support subsequent academic paper writing and further research on multilingual intelligent search systems.
The project will integrate cutting-edge cross-lingual pre-trained models, retrieval-augmented generation (RAG) pipelines, multi-level semantic alignment mechanisms, and intent parsing modules to break through language barriers in global information retrieval. Different from general search tools, this agent targets deep search tasks that require logical reasoning, cross-language knowledge fusion, and multi-source information verification, including cross-lingual academic literature retrieval, multilingual technical document mining, and cross-border open-domain fact checking.
During the project, we will construct a custom cross-lingual test dataset covering high-resource languages (English, Chinese) and typical low-resource regional languages, benchmark mainstream cross-lingual retrieval and generation models, and optimize core modules including cross-language semantic embedding alignment, noise filtering of retrieved content, and adaptive intent reasoning. The final deliverable will be a functional prototype of the cross-lingual deep search agent, along with comprehensive ablation experiment results and performance analysis reports, which can support subsequent academic paper writing and further research on multilingual intelligent search systems.
Supervisor
FUNG, May
Quota
2
Course type
UROP1000
UROP1100
UROP2100
UROP3100
UROP3200
UROP4100
Applicant's Roles
As the UROP student researcher for this summer project, I will undertake core hands-on development, experimental verification, and data analysis work throughout the full research cycle, under supervisor guidance. My specific responsibilities include:
1. Conduct systematic literature review on cross-lingual retrieval, multilingual LLM agents, and deep search RAG frameworks, summarize state-of-the-art methods, and sort out technical gaps and research innovation points.
2. Participate in the construction and cleaning of the project’s cross-lingual search dataset, including data sampling, annotation standard formulation, noise removal, and train/validation/test set partitioning.
3. Implement and adapt mainstream cross-lingual embedding models and RAG pipelines, complete model deployment, and build the basic framework of the deep search agent.
4. Design and conduct ablation experiments for core technical modules, test model performance on cross-lingual retrieval accuracy, intent matching rate, and answer reasoning rationality, and record experimental data in detail.
5. Identify and debug model failure cases in cross-lingual scenarios, propose targeted optimization strategies for semantic misalignment and cross-language information missing problems.
6. Sort out experimental results, summarize research findings, compile project progress reports and final technical documentation, and assist in sorting out preliminary research conclusions for follow-up academic outputs.
1. Conduct systematic literature review on cross-lingual retrieval, multilingual LLM agents, and deep search RAG frameworks, summarize state-of-the-art methods, and sort out technical gaps and research innovation points.
2. Participate in the construction and cleaning of the project’s cross-lingual search dataset, including data sampling, annotation standard formulation, noise removal, and train/validation/test set partitioning.
3. Implement and adapt mainstream cross-lingual embedding models and RAG pipelines, complete model deployment, and build the basic framework of the deep search agent.
4. Design and conduct ablation experiments for core technical modules, test model performance on cross-lingual retrieval accuracy, intent matching rate, and answer reasoning rationality, and record experimental data in detail.
5. Identify and debug model failure cases in cross-lingual scenarios, propose targeted optimization strategies for semantic misalignment and cross-language information missing problems.
6. Sort out experimental results, summarize research findings, compile project progress reports and final technical documentation, and assist in sorting out preliminary research conclusions for follow-up academic outputs.
Applicant's Learning Objectives
1. Technical Skill Acquisition: Master the core principles and engineering implementation of cross-lingual natural language processing (NLP), retrieval-augmented generation, and LLM agent development; proficiently grasp model fine-tuning, embedding alignment, vector database retrieval, and large-scale text data processing techniques.
2. Research Capability Improvement: Cultivate standardized academic research abilities, including literature screening and critical review, experimental scheme design, controlled variable testing, quantitative result analysis, and failure case analysis in AI research.
3. Cross-Domain Knowledge Integration: Build a systematic understanding of multilingual semantic alignment, deep user intent understanding, and intelligent search system architecture, and clarify the technical differences between traditional keyword search and modern LLM-based deep search.
4. Engineering & Academic Standardization: Learn end-to-end AI project development workflows from dataset construction, model implementation, experimental verification to result summarization, and develop rigorous scientific thinking and standardized technical writing skills.
5. Independent Problem-Solving Ability: Improve the ability to independently locate technical bottlenecks, query cutting-edge research resources, and propose feasible optimization solutions for complex cross-lingual NLP and agent system problems.
6. Cutting-Edge Research Vision: Keep abreast of the latest progress in top conferences (NeurIPS, ICLR, EMNLP) on multilingual AI and intelligent agent research, and lay a solid foundation for future advanced research in cross-lingual intelligence and generative AI search fields.
2. Research Capability Improvement: Cultivate standardized academic research abilities, including literature screening and critical review, experimental scheme design, controlled variable testing, quantitative result analysis, and failure case analysis in AI research.
3. Cross-Domain Knowledge Integration: Build a systematic understanding of multilingual semantic alignment, deep user intent understanding, and intelligent search system architecture, and clarify the technical differences between traditional keyword search and modern LLM-based deep search.
4. Engineering & Academic Standardization: Learn end-to-end AI project development workflows from dataset construction, model implementation, experimental verification to result summarization, and develop rigorous scientific thinking and standardized technical writing skills.
5. Independent Problem-Solving Ability: Improve the ability to independently locate technical bottlenecks, query cutting-edge research resources, and propose feasible optimization solutions for complex cross-lingual NLP and agent system problems.
6. Cutting-Edge Research Vision: Keep abreast of the latest progress in top conferences (NeurIPS, ICLR, EMNLP) on multilingual AI and intelligent agent research, and lay a solid foundation for future advanced research in cross-lingual intelligence and generative AI search fields.
Complexity of the project
Challenging