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Wednesday, January 10, 2024

Forwarded for publication. Fwd: Top Important LLM Papers for the Week from 01/01 to 07/01

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Marie Seshat Landry
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---------- Forwarded message ---------
From: Youssef Hosni from To Data & Beyond <youssefh@substack.com>
Date: Wed, Jan 10, 2024 at 12:27 AM
Subject: Top Important LLM Papers for the Week from 01/01 to 07/01
To: <marielandryx@gmail.com>


Stay Updated with Recent Large Language Models Research  ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌
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Top Important LLM Papers for the Week from 01/01 to 07/01

Stay Updated with Recent Large Language Models Research

Jan 10
 
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Every week, several top-tier academic conferences and journals showcased innovative research in computer vision, presenting exciting breakthroughs in various subfields such as image recognition, vision model optimization, generative adversarial networks (GANs), image segmentation, video analysis, and more.

This article provides a comprehensive overview of the most significant papers published in the first week of January 2024, highlighting the latest research and advancements in computer vision. Whether you're a researcher, practitioner, or enthusiast, this article will provide valuable insights into the state-of-the-art techniques and tools in computer vision.

Table of Contents:

  1. LLM Progress & Benchmarking

  2. LLM Fine Tuning

  3. LLM Reasoning

  4. LLM Training & Evaluation

  5. Transformers & Attention Based Models



1. LLM Progress & Benchmarking

  1. Boosting Large Language Model for Speech Synthesis: An Empirical Study

  2. LARP: Language-Agent Role Play for Open-World Games

  3. Improving Text Embeddings with Large Language Models

  4. PanGu-$Ï€$: Enhancing Language Model Architectures via Nonlinearity Compensation

  5. COSMO: COntrastive Streamlined MultimOdal Model with Interleaved Pre-Training

  6. TinyLlama: An Open-Source Small Language Model

  7. GeoGalactica: A Scientific Large Language Model in Geoscience

  8. Unicron: Economizing Self-Healing LLM Training at Scale

  9. LLaMA Beyond English: An Empirical Study on Language Capability Transfer

  10. DocLLM: A layout-aware generative language model for multimodal document understanding

  11. LLaMA Pro: Progressive LLaMA with Block Expansion

  12. LLM Augmented LLMs: Expanding Capabilities through Composition

  13. LLaVA-$φ$: Efficient Multi-Modal Assistant with Small Language Model

  14. A Comprehensive Study of Knowledge Editing for Large Language Models

2. LLM Fine Tuning

  1. Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models

  2. Astraios: Parameter-Efficient Instruction Tuning Code Large Language Models

3. LLM Reasoning

  1. Towards Truly Zero-shot Compositional Visual Reasoning with LLMs as Programmers

  2. Gemini in Reasoning: Unveiling Commonsense in Multimodal Large Language Models

4. LLM Training & Evaluation

  1. Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling Laws

  2. Understanding LLMs: A Comprehensive Overview from Training to Inference

5. Transformers & Attention Based Models

  1. Boundary Attention: Learning to Find Faint Boundaries at Any Resolution

  2. ICE-GRT: Instruction Context Enhancement by Generative Reinforcement-based Transformers


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WARNING: **Disclaimer:** This blog is for informational and educational purposes only and does not promote illegal or unethical espionage. The author is a researcher who analyzes publicly available information for her own clients and the public. The views expressed are the author's own and do not reflect any organization or government. The author makes no guarantees about the accuracy or completeness of the information provided. Reliance on the information is at your own risk. The author is not liable for any loss or damage resulting from the use of the information. The author reserves the right to modify or delete content without notice. By using this open source intelligence (OSINT) blog, you agree to these terms. If you disagree, please do not use this blog. -Marie Seshat Landry

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