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.

Can Language Models Solve Olympiad Programming? Researchers at Princeton University Introduce USACO Benchmark for Rigorously Evaluating Code Language Models

Code generation has emerged as a significant area for evaluating and deploying Large Language Models (LLMs). However, many of the current coding benchmarks, like...

A Detailed AI Study on State Space Models: Their Benefits and Characteristics along with Experimental Comparisons

The fields of Artificial Intelligence (AI) and Deep Learning have experienced significant growth in recent times. Following deep learning's domination, the Transformer architecture has...

Tango 2: The New Frontier in Text-to-Audio Synthesis and Its Superior Performance Metrics

With the introduction of some brilliant generative Artificial intelligence models, such as ChatGPT, GEMINI, and BARD, the demand for AI-generated content is rising in...

AutoCodeRover: An Automated Artificial Intelligence AI Approach for Solving Github Issues to Autonomously Achieve Program Improvement

Large Language Models (LLMs) have significantly advanced such that development processes have been further revolutionized by enabling developers to use LLM-based programming assistants for...

Small but Mighty: The Role of Small Language Models in Artificial Intelligence AI Advancement

In recent years, there has been a great inclination toward Large Language Models (LLMs) due to their amazing text generation, analysis, and classification capabilities....

Google AI Introduces an Efficient Machine Learning Method to Scale Transformer-based Large Language Models (LLMs) to Infinitely Long Inputs

Memory is significant for intelligence as it helps to recall past experiences and apply them to current situations. However, because of the way their...

LLM2Vec: A Simple AI Approach to Transform Any Decoder-Only LLM into a Text Encoder Achieving SOTA Performance on MTEB in the Unsupervised and Supervised...

Natural Language Processing (NLP) tasks heavily rely on text embedding models as they translate the semantic meaning of text into vector representations. These representations...

Researchers at the University of Cambridge Propose AnchorAL: A Unique Machine Learning Method for Active Learning in Unbalanced Classification Tasks

The abundance of web-scale textual data available has been a major factor in the development of generative language models, such as those pretrained as...

AutoWebGLM: A GPT-4-Outperforming Automated Web Navigation Agent Built Upon ChatGLM3-6B

Large Language Models (LLMs) have become essential tools for various intelligent agent tasks such as web navigation. The notion of self-governing digital agents, particularly...

CodeEditorBench: A Machine Learning System for Evaluating the Effectiveness of Large Language Models (LLMs) in Code Editing Activities

Coding-related jobs have led to the rapid advancement of Large Language Models (LLMs), with a focus on code editing. LLMs created specifically for coding...

Meet Sailor: A Family of Open Language Models Ranging from 0.5B to 7B Parameters for Southeast Asian (SEA) Languages

Large Language Models (LLM) have immense capabilities that have advanced remarkably in the last few years. Two primary causes of this increase are the...

‘Think-and-Execute’: A Machine Learning Framework that Encapsulates the Common Logical Structure of a Job Using Pseudocode for Efficient Reasoning in Large Language Models (LLMs)

In Large Language Models (LLMs), reasoning involves dissecting a problem's logical structure and turning it into a sequence of logical steps that lead to...

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