LG AI Research presented CADO research at ICML 2024, combining reinforcement learning and diffusion models to solve combinatorial optimization problems.
LG AI Research presented CADO, a study combining diffusion models with reinforcement learning to solve combinatorial optimization problems, at ICML 2024.
It introduces the latest research trends for overcoming the limitations of LLMs presented at ACL 2024 and LG AI Research's achievements.
Through the ACL 2024 tutorial session, we explore the latest research trends, including how to evaluate LLM-generated text and its vulnerabilities.
We introduce ChartInstruct, a general-purpose chart understanding model that comprehends the visual information in charts and performs complex reasoning.
We introduce ChartInstruct, a general-purpose chart vision-language model that applies Instruction Tuning to maximize chart understanding and reasoning capabilities.
This piece examines the latest techniques for optimizing inference efficiency and resource usage in LLMs, centered on research presented at ACL 2024.
This piece analyzes the latest efficiency research aimed at solving the computational cost and inference speed issues that come with the growing scale of LLMs.
We look at the limitations of AI-generated text detection technology presented at ACL 2024 and the latest research that addresses them.
This piece summarizes the trends and key methodologies of the latest AI-generated text detection technologies presented at ACL 2024.
This piece examines the methodologies for evaluating LLM faithfulness and the latest research trends for improving efficiency, as discussed at ACL 2024.
This piece examines the LLM reliability evaluation methodologies and research trends in complex task performance and efficiency presented at ACL 2024.
Through an ACL 2024 workshop, LG AI Research introduced the importance of developing LLMs that reflect cultural values and biases, along with related research.
This article looks at the latest research trends on how LLMs can reflect and measure diverse cultural values and social biases.
LG AI Research proposed Projection Regret, a technique that leverages Diffusion models to reduce interference from background information and improve novelty detection performance.
LG AI Research proposed Projection Regret, a technique that uses a Diffusion model to reduce background noise and improve anomaly detection performance.
LG AI Research has introduced an AI Red Team that checks vulnerabilities from an offensive perspective to enhance the security and reliability of generative AI.
It introduces the concept of AI Red Team and its step-by-step attack strategies for detecting and defending against vulnerabilities in generative AI.
LG AI Research conducts research to define and systematically classify the practical risks that AI can cause through its AI Ethics Seminar.
This article analyzes Microsoft's and Google's AI toolkits to design trustworthy AI experiences and presents practical application methods.
To design trustworthy AI experiences, this analyzes Microsoft's and Google's AI Toolkits and presents practical application guidelines.
Explores the potential for autonomous collaboration among AI agents and the changes they could bring to social structures.
It analyzes the autonomous decision-making and cooperation patterns of AI Agents through simulations and presents the need for ethical mechanisms.
LG AI Research has released 3 EXAONE 3.5 open-source models that maximize performance and efficiency.
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