Author: Tanya Malhotra

Tanya Malhotra
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Tanya Malhotra is a final year undergrad from the University of Petroleum & Energy Studies, Dehradun, pursuing BTech in Computer Science Engineering with a specialization in Artificial Intelligence and Machine Learning. She is a Data Science enthusiast with good analytical and critical thinking, along with an ardent interest in acquiring new skills, leading groups, and managing work in an organized manner.

This AI Paper Presents a Direct Experimental Comparison between 8B-Parameter Mamba, Mamba-2, Mamba-2-Hybrid, and Transformer Models Trained on Upto 3.5T Tokens

AI Paper Summary
Transformer-based Large Language Models (LLMs) have emerged as the backbone of Natural Language Processing (NLP). These models have shown remarkable performance over a variety...

BiGGen Bench: A Benchmark Designed to Evaluate Nine Core Capabilities of Language Models

AI Paper Summary
A systematic and multifaceted evaluation approach is needed to evaluate a Large Language Model's (LLM) proficiency in a given capacity. This method is necessary...

With 700,000 Large Language Models (LLMs) On Hugging Face Already, Where Is The Future of Artificial Intelligence AI Headed?

AI Shorts
Large Language Models (LLMs) have taken over the Artificial Intelligence (AI) community in recent times. In a Reddit post, a user recently brought attention...

This AI Paper from China Proposes a Novel dReLU-based Sparsification Method that Increases Model Sparsity to 90% while Maintaining Performance, Achieving a 2-5× Speedup...

AI Paper Summary
Large Language Models (LLMs) have made substantial progress in the field of Natural Language Processing (NLP). By scaling up the number of model parameters,...

This AI Paper from China Propose ‘Magnus’: Revolutionizing Efficient LLM Serving for LMaaS with Semantic-Based Request Length Prediction

AI Shorts
Transformer-based generative Large Language Models (LLMs) have shown considerable strength in a broad range of Natural Language Processing (NLP) tasks. Numerous applications benefit from...

Deepening Safety Alignment in Large Language Models (LLMs)

AI Shorts
Artificial Intelligence (AI) alignment strategies are critical in ensuring the safety of Large Language Models (LLMs). These techniques often combine preference-based optimization techniques like...

Researchers at Stanford Introduce a Two-Step Framework for Linguistic Calibration of Long-Form Generations

AI Shorts
Large language models (LLMs) have the potential to lead users to make poor decisions, especially when these models provide incorrect information with high confidence,...

The Evolution of Chinese Large Language Models (LLMs)

AI Shorts
Pre-trained language model development has advanced significantly in recent years, especially with the advent of large-scale models. For languages such as English, there is...

Message-Passing Monte Carlo (MPMC): A New State-of-the-Art Machine Learning Model that Generates Low-Discrepancy Points

AI Shorts
Monte Carlo (MC) methods rely on repeated random sampling, so they are widely utilized for simulating and approximating complicated real-world systems. These techniques work...

This AI Research Discusses Achieving Efficient Large Language Models (LLMs) by Eliminating Matrix Multiplication for Scalable Performance

AI Paper Summary
Most neural network topologies heavily rely on matrix multiplication (MatMul), primarily because it is essential to many basic processes. Vector-matrix multiplication (VMM) is commonly...

PixelsDB: An Open-Source Data Analytic System that Allows Users without SQL expertise to Explore Data Efficiently

AI Paper Summary
Query-as-a-Service (QaaS), also called serverless query processing, is a method of running analytical queries on the cloud. Serverless query engines, like AWS Athena and...

This Machine Learning Research from Microsoft Introduces an Active Preference Elicitation Method for the Online Alignment of Large Language Models

AI Paper Summary
Large Language Models (LLMs) have significantly advanced in recent times, primarily because of their increased capacity to follow human commands efficiently. Reinforcement Learning from...
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