Author: Mohammad Asjad

Mohammad Asjad
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Asjad is an intern consultant at Marktechpost. He is persuing B.Tech in mechanical engineering at the Indian Institute of Technology, Kharagpur. Asjad is a Machine learning and deep learning enthusiast who is always researching the applications of machine learning in healthcare.

Advancing Reliable Question Answering with the CRAG Benchmark

Large Language Models (LLMs) have revolutionized Natural Language Processing (NLP), particularly in Question Answering (QA). However, hallucination remains a significant obstacle as LLMs may...

From Low-Level to High-Level Tasks: Scaling Fine-Tuning with the ANDROIDCONTROL Dataset

Large language models (LLMs) have shown promise in powering autonomous agents that control computer interfaces to accomplish human tasks. However, without fine-tuning on human-collected...

The Missing Piece: Combining Foundation Models and Open-Endedness for Artificial Superhuman Intelligence ASI

Recent advances in artificial intelligence, primarily driven by foundation models, have enabled impressive progress. However, achieving artificial general intelligence, which involves reaching human-level performance...

Researchers at UC Berkeley Propose a Neural Diffusion Model that Operates on Syntax Trees for Program Synthesis

Large language models (LLMs) have revolutionized code generation, but their autoregressive nature poses a significant challenge. These models generate code token by token, without...

Modeling Cultural Accumulation in Artificial Reinforcement Learning Agents

Cultural accumulation, the ability to learn skills and accumulate knowledge across generations, is considered a key driver of human success. However, current methodologies in...

Quantized Eigenvector Matrices for 4-bit Second-Order Optimization of Deep Neural Networks

Deep neural networks (DNNs) have achieved remarkable success across various fields, including computer vision, natural language processing, and speech recognition. This success is largely...

Meet Tsinghua University’s GLM-4-9B-Chat-1M: An Outstanding Language Model Challenging GPT 4V, Gemini Pro (on vision), Mistral and Llama 3 8B

Tsinghua University's Knowledge Engineering Group (KEG) has unveiled GLM-4 9B, a powerful new language model that outperforms GPT-4 and Gemini in various benchmarks. Developed...

Parrot: Optimizing End-to-End Performance in LLM Applications Through Semantic Variables

Large language models (LLMs) possess advanced language understanding, enabling a shift in application development where AI agents communicate with LLMs via natural language prompts...

Researchers at Microsoft Introduce Aurora: A Large-Scale Foundation Model of the Atmosphere Trained on Over a Million Hours of Diverse Weather and Climate Data

Deep learning foundation models revolutionize fields like protein structure prediction, drug discovery, computer vision, and natural language processing. They rely on pretraining to learn...

Contextual Position Encoding (CoPE): A New Position Encoding Method that Allows Positions to be Conditioned on Context by Incrementing Position only on Certain Tokens...

Ordered sequences, including text, audio, and code, rely on position information for meaning. Large language models (LLMs), like the Transformer architecture, lack inherent ordering...

GNN-RAG: A Novel AI Method for Combining Language Understanding Abilities of LLMs with the Reasoning Abilities of GNNs in a Retrieval-Augmented Generation (RAG) Style

LLMs possess extraordinary natural language understanding capabilities, primarily derived from pretraining on extensive textual data. However, their adaptation to new or domain-specific knowledge is...

This AI Paper from Princeton and the University of Warwick Proposes a Novel Artificial Intelligence Approach to Enhance the Utility of LLMs as Cognitive...

Scientists studying Large Language Models (LLMs) have found that LLMs perform similarly to humans in cognitive tasks, often making judgments and decisions that deviate...

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