Agentic Horizons is an AI-hosted podcast exploring the cutting edge of artificial intelligence. Each episode dives into topics like generative AI, agentic syste...
FISHNET: Financial Intelligence from Sub-querying, Harmonizing, Neural-Conditioning, Expert Swarms, and Task Planning
In this episode, we dive into FISHNET, an advanced multi-agent system transforming financial analysis. Unlike traditional approaches that fine-tune large language models, FISHNET uses a modular structure with agents specialized in swarming, sub-querying, harmonizing, planning, and neural-conditioning. This design enables it to handle complex financial queries within a hierarchical agent-table data structure, achieving a notable 61.8% accuracy rate in solution generation.Key agents include:- Sub-querying Agent: Breaks down complex queries into manageable parts.- Task Planning Agent: Crafts initial query plans and collaborates with the Harmonizer Agent.- Harmonizer Agent: Orchestrates synthesis and plan execution, based on Expert Agent findings.- Expert Agents: Each specialized in specific U.S. regulatory filings (e.g., N-PORT, ADV).Trained on over 98,000 filings from EDGAR and IAPD, FISHNET’s performance is evaluated on retrieval precision, routing accuracy, and agentic success. This episode explores how FISHNET’s structured approach enables insightful, data-driven decisions, redefining financial analysis.https://arxiv.org/pdf/2410.19727
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12:33
LLMs Know More Than They Show
This episode discusses a research paper examining how Large Language Models (LLMs) internally encode truthfulness, particularly in relation to errors or "hallucinations." The study defines hallucinations broadly, covering factual inaccuracies, biases, and reasoning failures, and seeks to understand these errors by analyzing LLMs' internal representations.Key insights include:- Truthfulness Signals: Focusing on "exact answer tokens" within LLMs reveals concentrated truthfulness signals, aiding in detecting errors.- Error Detection and Generalization: Probing classifiers trained on these tokens outperform other methods but struggle to generalize across datasets, indicating variability in truthfulness encoding.- Error Taxonomy and Predictability: The study categorizes LLM errors, especially in factual tasks, finding patterns that allow some error types to be predicted based on internal representations.- Internal vs. External Discrepancies: There’s a gap between LLMs’ internal knowledge and their actual output, as models may internally encode correct answers yet produce incorrect outputs.The paper highlights that analyzing internal representations can improve error detection and offers reproducible results, with source code provided for further research.https://arxiv.org/pdf/2410.02707v3
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15:34
PDL: A Declarative Prompt Programming Language
This episode covers PDL (Prompt Declaration Language), a new language designed for working with large language models (LLMs). Unlike complex prompting frameworks, PDL provides a simple, YAML-based, declarative approach to crafting prompts, reducing errors and enhancing control.Key features include: • Versatility: Supports chatbots, retrieval-augmented generation (RAG), and agents for goal-driven AI. • Code as Data: Allows for program optimizations and enables LLMs to generate PDL code, as shown in a case study on solving GSMHard math problems. • Developer-Friendly Tools: Includes an interpreter, IDE support, Jupyter integration, and a live visualizer for easier programming.The episode concludes with a look at PDL’s future impact on speed, accuracy, and the evolving landscape of LLM programming.https://arxiv.org/pdf/2410.19135
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15:47
AI Self-Evolution Using Long Term Memory
The episode examines Long-Term Memory (LTM) in AI self-evolution, where AI models continuously adapt and improve through memory. LTM enables AI to retain past interactions, enhancing responsiveness and adaptability in changing contexts. Inspired by human memory’s depth, LTM integrates episodic, semantic, and procedural elements for flexible recall and real-time updates. Practical uses include mental health datasets, medical diagnosis, and the OMNE multi-agent framework, with future research focusing on better data collection, model design, and multi-agent applications. LTM is essential for advancing AI’s autonomous learning and complex problem-solving capabilities.https://arxiv.org/pdf/2410.15665
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23:28
Responsibility in a Multi-Value Strategic Setting
This episode delves into “multi-value responsibility” in AI, exploring how agents are attributed responsibility for outcomes based on contributions to multiple, possibly conflicting values. Key properties for a multi-value responsibility framework are discussed: consistency (an agent is responsible only if they could achieve all values concurrently), completeness (responsibility should reflect all outcomes), and acceptance of weak excuses (justifiable suboptimal actions).The authors introduce two responsibility concepts: • Passive Responsibility: Prioritizes consistency and completeness but may penalize justifiable actions. • Weak Responsibility: A more nuanced approach satisfying all properties, accounting for justifiable actions.The episode highlights that agents should minimize both passive and weak responsibility, optimizing for regret-minimization and non-dominance in strategy. This approach enables ethically aware, accountable AI systems capable of making justifiable decisions in complex multi-value contexts.https://arxiv.org/pdf/2410.17229
Agentic Horizons is an AI-hosted podcast exploring the cutting edge of artificial intelligence. Each episode dives into topics like generative AI, agentic systems, and prompt engineering, with content generated by AI agents based on research papers and articles from top AI experts. Whether you're an AI enthusiast, developer, or industry professional, this show offers fresh, AI-driven insights into the technologies shaping the future.