ETH Zurich Researchers Introduced EventChat: A CRS Using ChatGPT as Its Core Language Model Enhancing Small and Medium Enterprises with Advanced Conversational Recommender Systems
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by Aswin Ak
3h ago
Conversational Recommender Systems (CRS) are revolutionizing how users make decisions by offering personalized suggestions through interactive dialogue interfaces. Unlike traditional systems that present predetermined options, CRS allows users to dynamically input and refine their preferences, significantly reducing information overload. By incorporating feedback loops and advanced machine learning techniques, CRS provides an engaging and intuitive user experience. These systems are particularly valuable for small and medium-sized enterprises (SMEs) looking to enhance customer satisfaction an ..read more
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RoboMorph: Evolving Robot Design with Large Language Models and Evolutionary Machine Learning Algorithms for Enhanced Efficiency and Performance
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by Asif Razzaq
6h ago
The field of robotics is seeing transformative changes with the integration of generative methods like large language models (LLMs). These advancements enable the developing of sophisticated systems that autonomously navigate and adapt to various environments. The application of LLMs in robot design and control processes represents a significant leap forward, offering the potential to create robots that are more efficient & capable of performing complex tasks with greater autonomy.  Designing effective robot morphologies presents substantial challenges due to the expansive design spa ..read more
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Samsung Researchers Introduce LoRA-Guard: A Parameter-Efficient Guardrail Adaptation Method that Relies on Knowledge Sharing between LLMs and Guardrail Models
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by Mohammad Asjad
11h ago
Large Language Models (LLMs) have demonstrated remarkable proficiency in language generation tasks. However, their training process, which involves unsupervised learning from extensive datasets followed by supervised fine-tuning, presents significant challenges. The primary concern stems from the nature of pre-training datasets, such as Common Crawl, which often contain undesirable content. Consequently, LLMs inadvertently acquire the ability to generate offensive language and potentially harmful advice. This unintended capability poses a serious safety risk, as these models can produce coher ..read more
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Branch-and-Merge Method: Enhancing Language Adaptation in AI Models by Mitigating Catastrophic Forgetting and Ensuring Retention of Base Language Capabilities while Learning New Languages
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by Nikhil
13h ago
Language model adaptation is a crucial area in artificial intelligence, focusing on enhancing large pre-trained language models to work effectively across various languages. This research is vital for enabling these models to understand and generate text in multiple languages, which is essential for global AI applications. Despite the impressive performance of LLMs in English, their capabilities significantly drop when adapted to less prevalent languages, making additional adaptation techniques necessary. One of the significant challenges in adapting language models to new languages is catast ..read more
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Arena Learning: Transforming Post-Training of Large Language Models with AI-Powered Simulated Battles for Enhanced Efficiency and Performance in Natural Language Processing
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by Asif Razzaq
18h ago
Large language models (LLMs) have shown exceptional capabilities in understanding and generating human language, making substantial contributions to applications such as conversational AI. Chatbots powered by LLMs can engage in naturalistic dialogues, providing a wide range of services. The effectiveness of these chatbots relies heavily on high-quality instruction-following data used in post-training, enabling them to assist and communicate effectively with humans.  The challenge is the efficient post-training of LLMs using high-quality instruction data. Traditional methods involving hum ..read more
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Metron: A Holistic AI Framework for Evaluating User-Facing Performance in LLM Inference Systems
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by Aswin Ak
18h ago
Evaluating the performance of large language model (LLM) inference systems using conventional metrics presents significant challenges. Metrics such as Time To First Token (TTFT) and Time Between Tokens (TBT) do not capture the complete user experience during real-time interactions. This gap is critical in applications like chat and translation, where responsiveness directly affects user satisfaction. There is a need for a more nuanced evaluation framework that fully encapsulates the intricacies of LLM inference to ensure optimal deployment and performance in real-world scenarios. Current meth ..read more
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Optimizing Large Language Models (LLMs) on CPUs: Techniques for Enhanced Inference and Efficiency
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by Tanya Malhotra
21h ago
Large Language Models (LLMs) built on the Transformer architecture have recently attained important technological milestones. The remarkable skills of these models in comprehending and producing writing that resembles that of a human have had a significant impact on a variety of Artificial Intelligence (AI) applications. Although these models function admirably, there are many obstacles to successfully implementing them in low-resource contexts. The industry has given this problem a lot of attention, particularly in situations when access to GPU hardware resources is constrained. In these kin ..read more
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Meet Reworkd: An AI Startup that Automates End-to-end Data Extraction
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by Dhanshree Shripad Shenwai
21h ago
Collecting, monitoring, and maintaining a web data pipeline can be daunting and time-consuming when dealing with large amounts of data. Traditional approaches’ struggles can compromise data quality and availability with pagination, dynamic content, bot detection, and site modifications. Building an in-house technical staff or outsourcing to a low-cost nation are two common options for companies looking to meet their web data needs. While the latter usually could be more sustainable and necessitates heavy management supervision, the former can get pricey. Meet Reworkd AI, an AI startup that he ..read more
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FBI-LLM (Fully BInarized Large Language Model): An AI Framework Using Autoregressive Distillation for 1-bit Weight Binarization of LLMs from Scratch
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by Sana Hassan
22h ago
Transformer-based LLMs like ChatGPT and LLaMA excel in tasks requiring domain expertise and complex reasoning due to their large parameter sizes and extensive training data. However, their substantial computational and storage demands limit broader applications. Quantization addresses these challenges by converting 32-bit parameters to smaller bit sizes, enhancing storage efficiency and computational speed. Extreme quantization, or binarization, maximizes efficiency but reduces accuracy. While strategies like retaining key parameters or near-one-bit representation offer improvements, they sti ..read more
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Hyperion: A Novel, Modular, Distributed, High-Performance Optimization Framework Targeting both Discrete and Continuous-Time SLAM Applications
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by Niharika Singh
23h ago
In robotics, understanding the position and movement of a sensor suite within its environment is crucial. Traditional methods, called Simultaneous Localization and Mapping (SLAM), often face challenges with unsynchronized sensor data and require complex computations. These methods must estimate the position at discrete time intervals, making it difficult to handle data from various sensors that do not sync perfectly. There are existing methods that tackle these problems to some extent. Conventional SLAM techniques synchronize sensor data by converting it into discrete time intervals. This app ..read more
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