5th International Conference on NLP and Machine Learning Trends (NLMLT 2026)

Name: 5th International Conference on NLP and Machine Learning Trends (NLMLT 2026)
Start: 2026-08-15T09:00:00+05:30
End: 2026-08-16T18:00:00+05:30
Location: Melbourne

Schedule

Sat, 15 Aug, 2026 at 09:00 am to Sun, 16 Aug, 2026 at 06:00 pm

UTC+05:30

Location

Melbourne | Melbourne, VI

5th International Conference on NLP and Machine Learning Trends (NLMLT 2026)

August 15 ~ 16, 2026, Melbourne, Australia

https://nlmlt2026.org/

Scope

The 5th International Conference on NLP and Machine Learning Trends (NLMLT 2026) invites high quality research contributions that advance the rapidly evolving fields of Natural Language Processing and Machine Learning. As foundation models, multimodal systems and efficient learning architectures reshape the landscape of intelligent technologies, NLMLT 2026 provides a global forum for researchers, practitioners and industry innovators to share breakthroughs discuss emerging challenges and explore the next generation of NLP and ML solutions.

We welcome original research papers, survey articles, case studies and industrial applications that highlight significant progress in language technologies, learning algorithms, multimodal intelligence, efficient model design, responsible AI and real world deployment. Submissions may address theoretical foundations, methodological innovations, or practical implementations that push the boundaries of modern NLP and ML.

Authors are encouraged to contribute work related to the conference topics listed below, but submissions are not limited to these areas.

Topics of Interest

Large Language Models, Foundation Models and In Context Learning

Pre training, fine tuning and instruction tuning
In context learning (ICL) and meta learning
Prompt engineering, soft prompts and adapters
Retrieval Augmented Generation (RAG)
Long context models and memory architectures
Alignment, RLHF, DPO and safety tuning
Hallucination detection and mitigation
LLM security, adversarial robustness and red teaming
Tokenizer free and modular language models
Synthetic data generation, validation and governance

Small Language Models (SLMs) and Efficient Architectures

Focus on compact, efficient and deployable NLP/ML models
Small Language Models for edge and on device NLP
Domain specialized compact models
Efficient transformer alternatives (Mamba, RWKV, Hyena, S4, etc.)
Sparse models and Mixture of Experts (MoE)
Hardware aware optimization for NLP

NLP Tasks and Advanced Language Understanding

Machine Translation (speech to speech, low resource, multimodal)
Question Answering and Reading Comprehension
Summarization (factuality aware, abstractive)
Information Extraction and Named Entity Recognition
Sentiment, Emotion and Intent Analysis
Dialogue Systems and Conversational AI
Text Generation, Style Transfer and Controlled Generation
Semantic Parsing, Discourse and Pragmatics

Multimodal and Multisensory AI

Vision Language Models (VLMs)
Audio Language Models (ASR/TTS)
Video Language Understanding
Multimodal grounding and reasoning
Embodied AI and interactive multimodal agents

Reasoning, Planning and Neuro Symbolic NLP

Multi step reasoning and chain of thought modeling
Tool using LLM agents and autonomous workflows
Planning augmented NLP systems
Neuro symbolic reasoning and hybrid models
Mathematical reasoning and theorem assisted NLP
Multi agent communication and emergent behavior

Machine Learning Theory and Methods for NLP

Transformer architectures and sequence modeling
Representation learning and embeddings
Self supervised, semi supervised and weakly supervised learning
Generalization, robustness and domain adaptation
Continual, lifelong and curriculum learning
Probabilistic modeling and Bayesian deep learning
Optimization methods for large scale NLP

Efficient, Scalable and Green NLP

Model compression, pruning and distillation
Quantization and low precision inference
Distributed training and scalable optimization
Energy efficient NLP systems
Edge NLP and on device inference

Ethics, Fairness, Safety and Responsible NLP

Bias detection and mitigation
Fairness in multilingual and low resource NLP
Privacy preserving NLP (federated, encrypted computation)
Trustworthy evaluation and benchmark design
Safety critical NLP applications
AI governance, transparency and accountability

Multilingual, Cross Lingual and Low Resource NLP

Cross lingual transfer learning
Low resource language modeling
Multilingual LLMs and translation systems
Code switching and mixed language NLP
Typology aware modeling and linguistic diversity

Speech Language Unified Models

Speech language joint modeling
Speech to speech translation
Audio text alignment and grounding
Spoken dialogue systems

Applied NLP and ML Systems

Healthcare NLP
Legal, financial and scientific NLP
Recommender systems powered by NLP
Search, retrieval and ranking systems
Educational NLP
Social media analysis and misinformation detection

Data, Resources and Evaluation

Dataset creation, augmentation and curation
Synthetic data pipelines and validation
Annotation tools and human in the loop systems
Evaluation metrics and benchmark development
Benchmark contamination detection
Robustness under distribution shift

Paper Submission

Authors are invited to submit papers through the Submission System by May 24, 2026. Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this conference. The proceedings of the conference will be published by Computer Science Conference Proceedings in Computer Science & Information Technology (CS&IT) series(Confirmed).

Selected papers from NLMLT 2026, after further revisions, will be published in the special issue of the following journals