Touhidul Alam
NLP/AI Engineering Lead | Deep Expertise in LLMs and Generative AI Innovations
About
As an NLP Specialist, I transform business challenges into data-driven solutions. My expertise encompasses NLP, ML, and LLMs. At Accenture, I am dedicated to developing Generative AI architecture that deliver innovative solutions
Work Experience
AccentureMunichFrankfurt
NLP Specialist
Fraunhofer IISErlangen
Research Assistant
AccentureMunich
Student Intern
Smart Digital GmbHGerlingen
Student Assistant
Panacea Live LimitedDhaka
Backend Developer
Education
University of Stuttgart
Islamic University of Technology
Skills
Projects
A Voice-based LLM Crew Agent
A text/voice-based multi-agent system with RAG in specialized domain
LLM Multi-Agent for Search
Simple multi-agent system with external search API to search, analyze, generate content
Knowledge Graph LLM Agent
A knowledge-graph based LLM agent to perform search with different Cypher query
Tags generation
Simple prototyping of generating tags from forums with Open-source LLM
DA-Time
A domain-adapted temporal expression recognizer for the English Voice Assistant domain
AI Music Generation
A simple language modeling project to generate music trained by MIDI files pattern
Dialogue Act Classification
A Simple Dialogue Act Classification project using Keras from speech and text with an Ensemble model
Emotion Classification from Twitter text and image
Document Level Emotion Classification from Twitter Text and Images
Minimalistic Language Modeling
A Simple n-gram language modeling from scratch to understand the basic concept of language modeling
Publications
Enhancing Pipeline-Based Conversational Agents with Large Language Models
This study examines enhancing pipeline-based conversational agents with GPT-4, focusing on a hybrid approach that combines LLM strengths with existing system safeguards.
New Domain, Major Effort? How Much Data is Necessary to Adapt a Temporal Tagger to the Voice Assistant Domain
The study introduces DA-Time, a hybrid temporal tagger for voice assistants, proving that minimal in-domain data via transfer learning significantly enhances temporal expression recognition in diverse domains.
PÂTÉ: A Corpus of Temporal Expressions for the In-car Voice Assistant Domain
This study introduces a crowdsourcing method to collect and annotate natural-language commands with temporal expressions for AI voice assistants, differing from typical domains, to enhance their interaction with applications.