Artificial Discourse

<p>CORAL, a novel benchmark dataset for evaluating Retrieval-Augmented Generation (RAG) systems in a multi-turn conversational setting. The authors highlight the limitations of existing datasets in assessing...

<p> This research paper surveys small language models (SLMs) and explores their applications, design, training, and model compression techniques. The authors explain that while large language models (LLMs) have proven...

<p> This research explores whether transformers, a type of neural network architecture, can learn to reason implicitly over knowledge. The authors find that transformers can learn to reason implicitly, but only through...

<p>This research paper details the development of Llama 3, a large language model with improved capabilities in language understanding, code generation, mathematical reasoning, and multimodality. The paper emphasizes...

<p>Unlike traditional Multi-Layer Perceptrons (MLPs), which have fixed activation functions on nodes, KANs have learnable activation functions on edges. This seemingly simple change allows KANs to outperform MLPs in...

<p>The document describes the development of MMIE, a large-scale benchmark designed to evaluate the performance of Large Vision-Language Models (LVLMs) in interleaved multimodal comprehension and generation tasks. MMIE...

<p>This research paper proposes a novel method called Thought Preference Optimization (TPO) to train large language models (LLMs) to "think" before responding to user instructions. TPO utilizes a preference-based...

<p>The paper, "VIT-LENS: Towards Omni-modal Representations," introduces a novel approach to enable Artificial Intelligence (AI) agents to perceive information from various modalities beyond just vision and language. It...

<p> This research paper proposes a new method for efficiently training linear transformers, which are a type of neural network that uses linear attention to process sequences of data. Unlike traditional transformers...

<p>This research explores how the architecture of pre-trained language models influences their base capabilities, specifically focusing on the FFN-Wider Transformer architecture. The study identifies a key factor in...

<p>This research paper proposes a new method called Reinforcement Learning from Execution Feedback (RLEF) to improve the ability of large language models (LLMs) to generate code that successfully completes tasks. The...

<p>This research paper investigates how variations in the phrasing of prompts impact the performance of large language models (LLMs) across 120 tasks and five models. The study systematically analyzes six families of...

<p>LLaMA-Omni, designed to improve the seamless interaction between speech and large language models (LLMs). This model integrates a pretrained speech encoder, a speech adaptor, an LLM, and a streaming speech decoder...

<p>"Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Multimodal Models," details the development of a new family of multimodal language models (VLMs) called Molmo. Molmo is notable for its open-weight...

<p>This technical paper proposes a novel technique called Low-Rank Adaptation (LoRA) for adapting large language models (LLMs) to specific downstream tasks. LoRA addresses the challenge of fine-tuning LLMs, which...