As artificial intelligence (AI) technology continues to advance and permeate various aspects of society, it poses significant challenges to existing legal frameworks. One recurrent issue is how the law should regulate entities that lack intentions. Traditional legal principles often rely on the concept of mens rea, or the mental state of the actor, to determine…
Understanding how LLMs comprehend natural language plans, such as instructions and recipes, is crucial for their dependable use in decision-making systems. A critical aspect of plans is their temporal sequencing, which reflects the causal relationships between steps. Planning, integral to decision-making processes, has been extensively studied across domains like robotics and embodied environments. Effective utilization,…
Large language models (LLMs) face a critical challenge in their training process: the impending scarcity of high-quality internet data. Predictions suggest that by 2026, the available pool of such data will be exhausted, forcing researchers to turn to model-generated or synthetic data for training. This shift presents both opportunities and risks. While some studies have…
Claude 3.5 Sonnet by Anthropic AI has heralded a new era, surpassing its predecessors and contemporaries with unprecedented capabilities. This iteration of large language models (LLMs) demonstrates versatility and sophistication that exceed expectations and opens doors to applications previously deemed impractical or beyond reach. Let’s delve into ten remarkable examples that showcase Claude 3.5 Sonnet’s…
Large Language Models (LLMs) have made significant advances in the field of Information Extraction (IE). Information extraction is a task in Natural Language Processing (NLP) that involves identifying and extracting specific pieces of information from text. LLMs have demonstrated great results in IE, especially when combined with instruction tuning. Through instruction tuning, these models are…
Managing multiple agents in an AI system can be quite challenging. Each agent must communicate effectively, execute tasks reliably, and scale efficiently. This complex process often requires a robust framework to ensure smooth agent interaction and coordination. The available frameworks often fall short regarding ease of use, scalability, and flexibility. Many existing solutions require extensive…
In recent years, the proliferation of generative AI technologies has led to the development of various user interfaces that harness the power of AI to enhance productivity, creativity, and user interaction. These interfaces are becoming increasingly sophisticated, providing users new ways to engage with digital tools and platforms. Here are seven emerging generative AI user…
Large Language Models (LLMs) have gained significant prominence in the AI industry, revolutionizing various applications such as chat, programming, and search. However, the efficient serving of multiple LLMs has emerged as a critical challenge for endpoint providers. The primary issue lies in the substantial computational requirements of these models, with a single 175B LLM demanding…
The paper addresses the challenge of ensuring that large language models (LLMs) generate accurate, credible, and verifiable responses by correctly citing reliable sources. Existing methods often need help with errors and hallucinations, leading to incorrect or misleading information in generated responses. This research aims to improve the accuracy and reliability of LLM outputs by introducing…
Broadly neutralizing antibodies (bNAbs) are key in combating HIV-1. They target the virus’s envelope proteins and show promise in reducing viral loads and preventing infection. Despite their potential, identifying bNAbs remains labor-intensive, involving B-cell isolation and high-throughput next-generation sequencing. Only 255 bNAbs are known, and discovering new ones is challenging due to the virus’s rapid…