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Enhancing Autoregressive Decoding Efficiency: A Machine Learning Approach by Qualcomm AI Research Using Hybrid Large and Small Language Models

Marktechpost

Central to Natural Language Processing (NLP) advancements are large language models (LLMs), which have set new benchmarks for what machines can achieve in understanding and generating human language. One of the primary challenges in NLP is the computational demand for autoregressive decoding in LLMs.

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Can Synthetic Clinical Text Generation Revolutionize Clinical NLP Tasks? Meet ClinGen: An AI Model that Involves Clinical Knowledge Extraction and Context-Informed LLM Prompting

Marktechpost

Medical data extraction, analysis, and interpretation from unstructured clinical literature are included in the emerging discipline of clinical natural language processing (NLP). Even with its importance, particular difficulties arise while developing methodologies for clinical NLP. If you like our work, you will love our newsletter.

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A New AI Research Introduces Directional Stimulus Prompting (DSP): A New Prompting Framework to Better Guide the LLM in Generating the Desired Summary

Marktechpost

Natural language processing (NLP) has seen a paradigm shift in recent years, with the advent of Large Language Models (LLMs) that outperform formerly relatively tiny Language Models (LMs) like GPT-2 and T5 Raffel et al. on a variety of NLP tasks. Figure 1 depicts a sample of the summarising job.

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This AI Research Introduces GAIA: A Benchmark Defining the Next Milestone in General AI Proficiency

Marktechpost

It is a General AI Assistant that focuses on real-world questions, avoiding LLM evaluation pitfalls. With human-crafted questions that reflect AI assistant use cases, GAIA ensures practicality. By targeting open-ended generation in NLP, GAIA aims to redefine evaluation benchmarks and advance the next generation of AI systems.

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Meet FLM-101B: An Open-Source Decoder-Only LLM With 101 Billion Parameters

Marktechpost

Lately, Large language models (LLMs) are excelling in NLP and multimodal tasks but are facing two significant challenges: high computational costs and difficulties in conducting fair evaluations. These costs limit LLM development to a few major players, restricting research and applications.

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Meet DISC-FinLLM: A Chinese Financial Large Language Model (LLM) Based On Multiple Experts Fine-Tuning

Marktechpost

These Natural Language Processing (NLP) based models handle large and complicated datasets, which causes them to face a unique challenge in the finance industry. They are drawn from both self-constructed and available NLP datasets. The researchers have conducted multiple assessment benchmarks for evaluating DISC-FinLLM’s.

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Hello OLMo: A truly open LLM

Allen AI

I’m enthusiastic about getting OLMo into the hands of AI researchers,” said Eric Horvitz, Microsoft’s Chief Scientific Officer and a founding member of the AI2 Scientific Advisory Board.

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