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Introduction This blog explores Chain-of-Thought (CoT) prompting as a powerful technique for enhancing reasoning in large language models. By guiding models to break tasks into smaller steps, CoT mirrors human problem-solving. A study on shift ciphers reveals that CoT reasoning is influenced by factors such as task probability, frequency in

Introduction Retrieval is a complex task due to the diversity of queries and the importance of the relevance of the text being retrieved. There are primarily statistical-based retrieval and neural-based retrieval techniques. The paper we will be discussing today works on improving document embeddings for neural retrieval tasks. Traditional methods

Introduction Large Language Models (LLMs) such as GPT-4 and Llama2 generate human-like text, which has enabled a variety of applications. However, alongside this fluency, a major challenge remains hallucinations—situations where the model generates factually incorrect or unverifiable information. Hallucinations in LLMs are not one-dimensional but manifest in various forms,

Introduction LLMs excel in knowledge-intensive tasks but often struggle with niche or long-tail queries. RAG enhances LLMs by incorporating external knowledge, yet it faces a key challenge: imperfect retrieval. This occurs when retrieved information is incorrect, irrelevant, or conflicting, leading to unreliable results. In this blog, we explore an advanced

Introduction In this blog, we explore the paper on Source2Synth, a framework for synthetic data generation. Source2Synth addresses the common issues of low-quality synthetic data by grounding it in real-world sources and employing a multi-stage curation process. This approach enhances data quality and relevance. What is the problem with synthetic

DSPy is a declarative, self-improving framework for LLMs designed to streamline pipelines and aid broader LLM application development processes. The framework allows developers to define tasks and workflows in a high-level, declarative manner using their API. This process simplifies the need to specify “what needs to be done?” rather than

With the rapid adoption of AI agents and their workflows by enterprises and individuals alike in their daily lives, enabling agents to perform complex, long-horizon tasks with complex trajectories has become a significant challenge. The recent paper, “Agent Workflow Memory” introduces a novel approach to address this challenge by equipping