Large Language Models (LLMs) excel in generating human-like text, offering a plethora of applications from customer service automation to content creation. However, this immense potential comes with significant risks. LLMs are prone to adversarial attacks that manipulate them into producing harmful outputs. These vulnerabilities are particularly concerning given the models’ widespread use and accessibility, which… →
Mistral AI recently announced the release of Mistral Large 2, the latest iteration of its flagship model, which promises significant advancements over its predecessor. This new model excels in code generation, mathematics, and reasoning and offers enhanced multilingual support and advanced function-calling capabilities. Mistral Large 2 is designed to be cost-efficient, fast, and high-performing. It… →
Text retrieval is essential for applications like searching, question answering, semantic similarity, and item recommendation. Embedding or dense retrieval models play a key role in this process. The hard-negative mining method is used, to select negative passages for queries to train these models. It involves a teacher retrieval model to find passages related to the… →
Theorem proving in mathematics faces growing challenges due to increasing proof complexity. Formalized systems like Lean, Isabelle, and Coq offer computer-verifiable proofs, but creating these demands substantial human effort. Large language models (LLMs) show promise in solving high-school-level math problems using proof assistants, yet their performance still needs to improve due to data scarcity. Formal… →
Question answering (QA) is a crucial area in natural language processing (NLP), focusing on developing systems that can accurately retrieve and generate responses to user queries from extensive data sources. Retrieval-augmented generation (RAG) enhances the quality and relevance of answers by combining information retrieval with text generation. This approach filters out irrelevant information and presents… →
For small-to-mid-sized businesses (SMBs), the burden of manually executing day-to-day processes using folders of Excel files and third-party apps can be overwhelming. This time-consuming, error-prone method often hinders scaling. In the freight forwarding industry, for instance, the majority of tasks revolve around managing customer relationships, monitoring inventories, and scheduling delivery dates. The transition to Manaflow,… →
Video large language models (LLMs) have emerged as powerful tools for processing video inputs and generating contextually relevant responses to user commands. However, these models face significant challenges in their current methodologies. The primary issue lies in the high computational and labeling costs associated with training on supervised fine-tuning (SFT) video datasets. Also, existing Video… →
Large Language Models (LLMs) have revolutionized AI with their ability to understand and generate human-like text. Their rise is driven by advancements in deep learning, data availability, and computing power. Learning about LLMs is essential to harness their potential for solving complex language tasks and staying ahead in the evolving AI landscape. This article lists… →
Current AI task management methods, such as AutoGPT, BabyAGI, and LangChain, typically rely on free-text outputs, which can be lengthy and less efficient. These frameworks often face challenges in maintaining context and managing the vast action space associated with arbitrary tasks. This research paper addresses the limitations of existing agentic frameworks in natural language processing… →
Automated Machine Learning has become essential in data-driven decision-making, allowing domain experts to use machine learning without requiring considerable statistical knowledge. Nevertheless, a major obstacle that many current AutoML systems encounter is the efficient and correct handling of multimodal data. There are currently no systematic comparisons between different information fusion approaches and no generalized frameworks… →