Mabble Rabble
random ramblings & thunderous tidbits
1 April 2025
April Fool's
Generative Multiagent Papers
- Generative Agents: Interactive Simulacra of Human Behavior
- ReAct: Synergizing Reasoning and Acting in Language Models
- Tree of Thoughts: Deliberate Problem Solving with Large Language Models
- AutoGen: Enabling Next-Gen Multi-Agent Autonomy
- When One LLM Drools, Multi-LLM Collaboration Rules
- MultiAgentBench: Evaluating the Collaboration and Competition of LLM Agents
- Why Do Multi-Agent LLM Systems Fail?
- ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate
- Cooperate or Collapse: Emergence of Sustainable Cooperation in a Society of LLM Agents
- Talk Structurally, Act Hierarchically: A Collaborative Framework for LLM Multi-Agent Systems
- Agents Thinking Fast and Slow: A Talker-Reasoner Architecture for Language Model Agents
- Model Swarms: Collaborative Search to Adapt LLM Experts via Swarm Intelligence
- Automated Design of Agentic Systems
- MAGIS: LLM-Based Multi-Agent Framework for GitHub Issue Resolution
- AutoKaggle: A Multi-Agent Framework for Autonomous Data Science Competitions
- SciAgents: Automating Scientific Discovery through Multi-Agent Intelligent Graph Reasoning
- Mora: Enabling Generalist Video Generation via A Multi-Agent Framework
- PC-Agent: A Hierarchical Multi-Agent Framework for Complex Task Automation on PC
- Enhancing Reasoning with Collaboration and Memory in Large Language Models
- Diffusion Augmented Agents: A Framework for Efficient Exploration and Transfer Learning
- The Fellowship of the LLMs: Multi-Agent Workflows for Synthetic Preference Optimization
- Multi-agent Architecture Search via Agentic Supernet
- A Survey on LLM-based Multi-Agent System: Recent Advances and New Frontiers in Application
- Large Language Model Based Multi-agents: A Survey of Progress and Challenges
- From RAG to Multi-Agent Systems: A Survey of Modern Approaches in LLM Development
31 March 2025
Non-Compliance in Static SQL
Semantic Role Labeling
- A New Method for Cross-Lingual-based Semantic Role Labeling
- Semantic Role Labeling: A Systematical Survey
- Learning Semantic Role Labeling from Compatible Label Sequences
- BERTie Bott's Every Flaver Labels
- Label Definitions Improve Semantic Role Labeling
- PriMeSRL-Eval
- Semantic Role Labeling Guided Out-of-distribution Detection
- End-to-end Learning of Semantic Role Labeling using Neural Networks
- Neural Semantic Role Labeling with Dependency Path Embeddings
- Jointly Predicting Predicates and Arguments in Neural Semantic Role Labeling
- A Full End-to-End Semantic Role Labeler, Syntax-agnostic Over Syntax-aware?
- How to Best Use Syntax in Semantic Role Labelling
- Structured Tuning for Semantic Role Labeling
- End-to-end Semantic Role Labeling with Neural Transition-based Model
- The Necessity of Parsing for Predicate Argument Recognition
- Calibrating Features for Semantic Role Labeling
- Support Vector Learning for Semantic Argument Classification
- Introduction to the CoNLL-2005 Shared Task: Semantic Role Labeling
29 March 2025
Water from Thin Air
Why Adsense is so Bad
28 March 2025
ShadowDragon's SocialNet
Knowledge Graphs and LLMs
Digital Nomadism
27 March 2025
Chinese Social Media
RAG and Legal Documents
The legal field is notorious for its complexity, with vast amounts of information scattered across statutes, case law, and legal commentaries. Navigating this maze can be a daunting task for even the most seasoned lawyers. However, the Retrieval Augmented Generation (RAG) and Large Language Models (LLMs) offers a promising solution to streamline legal research and analysis.
RAG leverages the power of LLMs by combining them with external knowledge sources.
This approach offers several advantages. Firstly, RAG enables LLMs to access and process the most up-to-date information directly from the source. This ensures that the answers provided are accurate and compliant with the latest legal developments. Secondly, by grounding the LLM's responses in specific legal documents, it enhances transparency and accountability. Users can easily verify the LLM's reasoning by referring to the cited passages.
Furthermore, RAG can significantly improve the efficiency of legal research and analysis.
However, implementing RAG for legal research also presents certain challenges. Ensuring the accuracy and completeness of the knowledge base is crucial. The legal landscape is constantly evolving, requiring frequent maintenance and updates to the underlying data. Additionally, addressing potential biases in the data and ensuring fairness and ethical considerations in the LLM's responses are important considerations.
Despite the challenges, the potential benefits of using RAG and LLMs to navigate legal cases and guidebooks are huge. By leveraging the power of AI and machine learning, lawyers can enhance their understanding of complex legal issues, improve the quality of their legal advice, and ultimately provide better service to their clients.
FCA Handbook with RAG
Google Customer Service
Tacred for Relation Extraction
26 March 2025
Great Retail Deception
25 March 2025
24 March 2025
Third-Party Licensing Services
- LicenseSpring
- 10Duke
- Cryptolens
- PACE
- Wibu
- Keygen
- LicenseOne
- SoftwareKey
- QuickLicense
- ProtectionMaster
- SafetNet Sentinel
- Trelica
- OpenLM
- Software Shield
- Zluri
- Flexera
- Ivanti
- Snow
- AssetSonar
- Reprise
- Torii
- AWS license manager
- ServiceNow