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.

pEBR: A Novel Probabilistic Embedding based Retrieval Model to Address the Challenges of Insufficient Retrieval for Head Queries and Irrelevant Retrieval for Tail Queries

AI Paper Summary
Creating a common semantic space where queries and items can be represented as dense vectors is the main goal of embedding-based retrieval. Instead of...

Enhancing Artificial Intelligence Reasoning by Addressing Softmax Limitations in Sharp Decision-Making with Adaptive Temperature Techniques

AI Paper Summary
The ability to generate accurate conclusions based on data inputs is essential for strong reasoning and dependable performance in Artificial Intelligence (AI) systems. The...

Top 30 Artificial Intelligence (AI) Tools for Data Analysts

Editors Pick
The development of Artificial Intelligence (AI) tools has transformed data processing, analysis, and visualization, increasing the efficiency and insight of data analysts' work. With...

Top 10 Platforms to Practice Python

Editors Pick
Python is a high-level, flexible programming language that is well-known for its extensive ecosystem, ease of use, and readability. Python’s vast libraries and frameworks...

Nova: An Iterative Planning and Search Approach to Enhance Novelty and Diversity of Large Language Model (LLM) Generated Ideas

AI Paper Summary
Innovation in science is essential to human progress because it drives developments in a wide range of industries, including technology, healthcare, and environmental sustainability....

MIRAGE-Bench: An Automatic Multilingual Benchmark for Retrieval-Augmented Generation Systems

AI Paper Summary
Large Language Models (LLMs) have emerged as crucial tools for handling intricate information-seeking queries due to techniques that improve both retrieval and response generation....

Can LLMs Follow Instructions Reliably? A Look at Uncertainty Estimation Challenges

AI Paper Summary
Large Language Models (LLMs) have potential applications in education, healthcare, mental health support, and other domains. However, their accuracy and consistency in following user...

FedPart: A New AI Technique for Enhancing Federated Learning Efficiency through Partial Network Updates and Layer Selection Strategies

AI Paper Summary
Federated Learning is a distributed method of Machine Learning that puts user privacy first by storing data locally and never centralizing it on a...

Layer-of-Thoughts Prompting (LoT): A Unique Approach that Uses Large Language Model (LLM) based Retrieval with Constraint Hierarchies

AI Paper Summary
Utilizing Large Language Models (LLMs) through different prompting strategies has become popular in recent years. However, many current methods frequently offer very general frameworks...

Meet SynPO: A Self-Boosting Paradigm that Uses Synthetic Preference Data for Model Alignment

AI Paper Summary
Alignment with human preferences has led to significant progress in producing honest, safe, and useful responses from Large Language Models (LLMs). Through this alignment...

This Machine Learning Research Discusses How Task Diversity Shortens the In-Context Learning (ICL) Plateau

AI Paper Summary
A primary feature of sophisticated language models is In-Context Learning (ICL), which allows the model to produce answers based on input instances without being...

DaWin: A Training-Free Dynamic Weight Interpolation Framework for Robust Adaptation

AI Paper Summary
Maintaining the model's capacity to manage changes in data distribution, i.e., the ability to function effectively even when presented with data that is different...
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