LG AI Research Launches EXAONE 3.5: Open-Source Bilingual AI Models for Superior Instruction Following & Long Context Understanding in Generative AI

Introduction

The release of EXAONE 3.5 by LG AI Research marks a significant leap in the field of generative AI, with enhancements that address key challenges in AI performance, including instruction-following capabilities and long context understanding. This new version follows the August launch of EXAONE 3.0, building on its success by further improving its performance across 20 different benchmarks. EXAONE 3.5's open-source release makes it a valuable resource for the global AI research community, promoting collaboration and innovation.

Notably, EXAONE 3.5 introduces three distinct models with varying sizes and capabilities, from ultra-lightweight models for on-device applications to high-performance models designed for specialized tasks. The integration of Retrieval-Augmented Generation (RAG) technology reduces hallucinations and enhances accuracy, while Multi-step Reasoning allows the AI to break down complex inquiries for more reliable answers.

The open-source nature of EXAONE 3.5 not only accelerates AI research but also fosters an ecosystem that can rapidly scale to meet diverse needs, from academic applications to enterprise-level AI services. This is further demonstrated by the introduction of LG's ChatEXAONE, an enterprise AI agent that integrates the new model, offering capabilities such as real-time web information search, document summarization, and even coding support.

With EXAONE 3.5, LG AI Research is solidifying its position as a leader in the AI space, providing cutting-edge tools that contribute to the advancement of both academic and commercial AI technologies.

EXAONE 3.5 stands out for its unique combination of bilingual capabilities, advanced instruction-following features, and exceptional long-context understanding, making it a powerful tool in the generative AI field.

1. Bilingual Capabilities: EXAONE 3.5 is designed to excel in both English and Korean, offering seamless language processing across these two languages. Its bilingual nature is backed by its training on large, diverse datasets, ensuring its performance in real-world use cases, math, coding, and general understanding surpasses many competing models in both languages. The model is fine-tuned with human preferences to ensure the best results across languages, enabling it to understand and generate contextually accurate responses.

   
   

2. Advanced Instruction Following: EXAONE 3.5 demonstrates exceptional instruction-following abilities, which have been evaluated in tasks like reasoning, coding, and mathematical problem-solving. The model has been trained using advanced techniques such as Direct Preference Optimization (DPO), which refines the model's response selection based on human feedback. This approach enables EXAONE to better align with user intentions and provide clear, context-aware answers​.

   
   

3. Long-Context Understanding: One of the standout features of EXAONE 3.5 is its ability to handle long-context prompts effectively. This is crucial for real-world applications where maintaining coherence across lengthy interactions is necessary. Whether it’s parsing multi-turn dialogues or complex problem-solving over extended text inputs, EXAONE can maintain context over longer exchanges, making it highly suitable for applications that require deep understanding of intricate details across extended interactions​.

   
   

These features collectively empower EXAONE 3.5 to perform at a high level in both practical and specialized tasks, positioning it as a leading AI model for both global and bilingual contexts.

The release of EXAONE 3.5 marks a significant leap forward in generative AI, strengthening LG AI Research’s position as a global leader in this rapidly evolving field. This model, which builds on previous iterations, sets new benchmarks in efficiency, performance, and scalability, demonstrating LG's commitment to driving AI innovation. EXAONE 3.5 excels in multiple areas, including real-world use cases, coding, and mathematical problem-solving, outpacing other global models like Meta’s Llama 3.1 and Google's Gemma 2. By achieving top-tier performance in these areas, it establishes itself as a powerful tool for industry applications that require robust AI models.

One of the standout features of EXAONE 3.5 is its bilingual capabilities, with the model performing exceptionally well in both English and Korean. This bilingual strength enhances its accessibility and usability, making it a versatile solution for global enterprises and researchers. Furthermore, LG AI Research has made the model open-source, fostering collaboration and growth within the AI community. This open approach encourages academic institutions and other research organizations to leverage the model for advancing AI technology and addressing complex, real-world challenges.

The model’s optimized performance comes with substantial reductions in processing time, memory usage, and operational costs, making it more accessible for widespread use, from small devices to large-scale enterprise applications. The open-source release also underscores LG's dedication to not only advancing AI capabilities but also ensuring that the benefits of this technology are widely distributed, which will strengthen global leadership in generative AI by contributing to the growth of a vibrant research ecosystem.

What’s New in EXAONE 3.5

EXAONE 3.5 introduces significant updates compared to EXAONE 3.0, continuing to push the boundaries of generative AI capabilities. One of the key differences is the expansion of model options for varied use cases. EXAONE 3.0 had a single 7.8 billion parameter model, whereas EXAONE 3.5 includes three models with varying sizes, ranging from a lightweight 2.4 billion parameter model for on-device use, to a high-performance 32 billion parameter model for specialized applications. This diversity allows for greater flexibility, catering to different operational needs, from basic tasks to complex, resource-intensive applications.

Another major upgrade is in performance. EXAONE 3.5 excels in real-world usability across 20 benchmarks, including capabilities in long text processing, coding, and math. Its ability to handle texts up to around 100 pages is a marked improvement over EXAONE 3.0. This version also integrates advanced Retrieval-Augmented Generation (RAG) technology, which enhances accuracy by generating answers based on real-time web searches or uploaded documents. Additionally, EXAONE 3.5 improves logical reasoning capabilities with multi-step reasoning, allowing the AI to break down complex queries step by step.

Furthermore, EXAONE 3.5 introduces specialized features like "Deep" and "Dive" modes in its enterprise product, ChatEXAONE. These features allow the AI to process complex, multi-faceted inquiries and refine answers based on specific contexts such as global, academic, or multimedia sources.

These updates position EXAONE 3.5 as a more powerful tool, offering both flexibility and specialized performance, with applications that range from enhancing productivity in enterprises to fostering innovation in AI research.

The performance improvements in EXAONE 3.5, especially in long-context handling and instruction following, reflect significant advancements in how models process and leverage extended inputs for more accurate outputs. One of the most notable enhancements is the model's ability to manage longer contexts, addressing a common issue in AI models known as the "U-shaped performance curve." This curve often occurs when relevant information is buried in the middle of a lengthy input, causing models to struggle with access. By refining how the EXAONE 3.5 handles this issue, the model now offers more reliable responses even in tasks requiring large amounts of context, such as document summarization or complex Q&A tasks..

Additionally, the improved instruction-following capabilities of EXAONE 3.5 make it more effective in multi-turn conversations and complex tasks. Instruction-following is a critical function in AI models, as it ensures that the model understands and acts on specific user directives..

These upgrades mark a step toward overcoming the traditional limitations of language models in real-world, high-context environments, boosting their ability to engage in sophisticated, long-form interactions while staying accurate and efficient. These improvements are also expected to benefit specific sectors that rely on accurate, multi-turn interaction, such as legal document analysis or scientific research.

EXAONE 3.5, developed by LG AI Research, has demonstrated remarkable multilingual capabilities, particularly in English and Korean. This bilingual model, designed for global AI excellence, has been trained with extensive datasets in both languages, making it highly proficient in instruction-following tasks and long-context understanding.

The model's performance has been rigorously evaluated against standard benchmarks in both English and Korean. EXAONE 3.5 has outperformed many competitors in real-world use cases, scoring significantly higher than other models such as Llama 3.1 and Gemma 2, particularly in English. For example, in real-world use case tasks, EXAONE scored 57.5, which was the highest among models in its category.

In the specific case of Korean, EXAONE 3.5 again led the field, with a score of 8.77 in real-world use cases, a clear indicator of its strength in handling the linguistic complexities of Korean. Additionally, EXAONE has demonstrated advanced proficiency in more technical categories like math and coding, further reinforcing its superiority in both languages.

These benchmarks highlight EXAONE 3.5's ability to handle diverse language tasks with exceptional precision, setting it apart as a leader in the bilingual AI model space.

Innovative Features of EXAONE 3.5

EXAONE's bilingual capabilities are a key highlight of its latest iteration. With robust support for both Korean and English, EXAONE is positioned to bridge linguistic and technological gaps, making it an ideal model for diverse applications in global markets. The model has shown impressive performance in both languages, particularly excelling in tasks related to natural language processing, coding, and even specialized fields such as math and chemistry.

In a global context, this bilingual strength positions EXAONE as a versatile tool for cross-border innovation. Korean businesses and researchers, in particular, will benefit from a language model that understands the intricacies of the Korean language, enabling more precise applications in domestic industries. At the same time, EXAONE's competence in English ensures it can seamlessly integrate into the global AI ecosystem, where English remains the dominant language for tech innovation.

The bilingual model's design also opens up opportunities for applications that require deep cross-lingual understanding. It can support AI-driven tools in sectors like law, education, and medicine, not only enhancing research in these fields but also fostering international collaborations. Additionally, EXAONE's potential to scale and be fine-tuned for specific markets ensures that it can provide tailored solutions for businesses looking to expand globally, whether they're in South Korea or anywhere else.

This bilingual flexibility, combined with EXAONE’s open-source nature, positions it as a game-changer for the future of global AI development.

EXAONE 3.5 excels in long-context understanding, a critical capability for managing complex tasks that span extensive text inputs. With the ability to process up to 32K tokens, EXAONE 3.5's long-context proficiency enables it to maintain coherent and contextually relevant output even when handling large amounts of information. This is essential in applications requiring comprehensive dialogue management, complex problem-solving, or detailed content analysis over extended interactions.

The model's attention mechanism, which includes advanced local and global attention strategies, allows it to focus on both smaller chunks and the broader context across longer passages. This ensures that even subtle shifts in meaning or intent across large blocks of text are tracked and incorporated, providing more precise and contextually grounded responses.

In practical terms, EXAONE 3.5's long-context capabilities are vital in real-world scenarios like document summarization, program synthesis, and web interaction tasks, where long inputs need to be processed accurately. For example, the model’s ability to handle lengthy HTML documents with deep semantic comprehension has been highlighted in tasks such as web-based program generation and automation.

The decision to make EXAONE 3.5 an open-source bilingual frontier AI-level model is highly significant, particularly for the global AI research community. By releasing such powerful models as open-source, LG AI Research is enabling researchers, developers, and even startups to innovate and experiment with the latest AI capabilities without the constraints typically associated with proprietary models. This fosters an environment of collaboration, making cutting-edge AI technology more accessible to a wider audience. Open-source initiatives like this ensure that the models can be tested, improved, and adapted by a diverse set of users, increasing the speed and breadth of advancements in AI.

The accessibility of these models supports the democratization of AI technology, empowering smaller teams and independent researchers who might not have the resources of large corporations. It also encourages the sharing of knowledge and solutions to common challenges such as ethical AI development, data privacy, and fairness in decision-making. Open-sourcing also helps ensure that AI tools remain transparent and accountable, allowing the community to identify and correct any biases or flaws in the models.

Moreover, open-source models can drive innovation in various sectors, from healthcare to entertainment, by allowing developers to tailor the models for specific applications or to integrate them into existing systems. This also accelerates the development of AI applications, as developers can freely use and iterate on the code without needing to build models from scratch. As EXAONE 3.5 becomes more widely utilized, it will likely spark new breakthroughs, use cases, and interdisciplinary collaborations that push the boundaries of generative AI.

By making EXAONE 3.5 available to the broader community, LG AI Research is also positioning itself as a leader in the effort to integrate AI into global technological ecosystems, setting a new standard for open collaboration in AI development.

Performance and Benchmarks

EXAONE 3.5, a language model developed by LG AI Research, has demonstrated solid performance across several key AI benchmarks, underscoring its potential in real-world applications, such as reasoning, coding, and multilingual tasks.

In terms of reasoning, EXAONE 3.5 has shown strong results on various language understanding benchmarks, like MT-Bench and Arena-Hard-v0.1, where it outperformed similar models in areas such as logical inference and context comprehension. On multilingual tasks, the model exhibits competitive performance, notably in Korean, with an edge in cross-lingual understanding when compared to other models of a similar size​.

For coding, EXAONE 3.5 is adept at solving technical queries. It has been tested against industry-standard coding benchmarks like HumanEval, which challenges models with programming tasks. EXAONE's coding proficiency is aligned with top models, demonstrating its capability to generate functional code for a variety of programming problems​.

The model’s real-world applications are a result of comprehensive training, utilizing extensive bilingual datasets and instruction fine-tuning, making EXAONE 3.5 an excellent tool for industries requiring high-level reasoning, programming, and multilingual support​. However, it is important to note that, like most advanced models, EXAONE may occasionally generate outputs that require careful monitoring to ensure quality and accuracy, especially in sensitive areas​.

When comparing EXAONE 3.5 with models like Llama and Mistral, EXAONE stands out in several key areas. While Llama (from Meta) and Mistral (from the French startup Mistral AI) each have their strengths, EXAONE’s design and capabilities offer unique advantages.

Llama Models: Meta’s Llama series excels in multilingual support, offering eight languages with models like Llama 3.1 in sizes ranging from 8B to 405B parameters. Llama models also boast impressive context lengths of up to 128,000 tokens, making them suitable for long-term memory tasks. However, these models lack advanced multimodal capabilities, which limits their versatility in tasks like image understanding​.

Mistral AI Models: Mistral AI’s offerings, such as Mistral Large, are highly customizable and open-source, which is appealing to developers who need tailored solutions. Mistral’s models are known for their efficiency, providing high performance at lower costs, making them ideal for enterprise applications. Mistral also supports portability, allowing deployment via serverless APIs or on-premises. However, its focus is primarily on reasoning and efficiency rather than advanced instruction-following and contextual understanding​.

EXAONE's Strengths: EXAONE 3.5, developed by LG AI Research, differentiates itself through a balanced combination of long-context understanding and superior instruction-following abilities, making it suitable for complex, real-world tasks. Its open-source bilingual models, designed to handle both English and other major languages, set EXAONE apart as an ideal choice for global AI applications that require nuanced comprehension and fluent, responsive interaction. Unlike Llama’s open deployment or Mistral’s focus on efficiency, EXAONE is particularly built to enhance the long-term usability and seamless deployment of AI in dynamic, multi-lingual environments.

In conclusion, while Llama and Mistral have their niche strengths in language support and cost-efficiency respectively, EXAONE’s focus on high-level instruction-following, extensive context handling, and multi-lingual proficiency makes it a robust contender for leadership in generative AI innovation.

Applications and Use Cases

EXAONE 3.5 offers impressive capabilities that can greatly enhance real-world applications across multiple sectors. Here's how it stands out in various industries:

1. AI-Driven Customer Service: EXAONE 3.5 can provide a major boost to customer support through its advanced conversational capabilities. It enhances chatbot systems, enabling them to understand and respond with greater context and accuracy. This translates to smoother customer interactions, faster issue resolution, and improved satisfaction. AI-driven customer service solutions using EXAONE 3.5 can analyze queries, anticipate needs, and provide personalized responses, which helps reduce human error and waiting times.

2. Content Generation: With its generative capabilities, EXAONE 3.5 is a powerful tool for content creation. Whether it's drafting articles, generating marketing copy, or creating educational materials, EXAONE 3.5 excels in producing relevant and engaging content tailored to specific audiences. It can assist in brainstorming ideas, refining written content, or even creating large amounts of text in short periods, making it a valuable asset for businesses looking to scale their content output. The model's ability to adapt its tone and style according to the intended use case (e.g., formal or casual) makes it versatile for marketing, advertising, and even personalized email campaigns.

3. Enhancing Software Development: EXAONE 3.5 can be a game-changer for developers, enabling faster and more efficient software creation. It can assist in code generation, automating repetitive tasks, and suggesting improvements to existing code. Its ability to understand complex code structures and provide recommendations helps developers reduce debugging times and create optimized, error-free applications. Moreover, EXAONE can be integrated into DevOps pipelines to ensure continuous improvement and testing, making software development workflows more streamlined and efficient​.

4. Financial Services and Risk Management: EXAONE 3.5 can help in analyzing large data sets to create predictive models for investments or identify potential financial risks. It can quickly draft financial reports or interpret complex regulations, allowing financial institutions to focus more on strategy and less on administrative tasks. Its advanced pattern recognition capabilities also enable better fraud detection, risk assessment, and decision-making​.

   
   

In these ways, EXAONE 3.5 helps various sectors improve productivity, reduce costs, and offer better services. Its ability to understand, generate, and analyze large volumes of data and interactions makes it a versatile and valuable tool across industries.

The bilingual capabilities of EXAONE 3.5 present significant potential for industries such as healthcare, finance, and education, enabling more accessible and efficient services. In healthcare, for example, AI can facilitate seamless communication between medical professionals and patients who speak different languages, improving diagnostic accuracy, treatment explanations, and patient care in multicultural environments. Additionally, it can enhance data management by allowing systems to interpret and process medical records in multiple languages, increasing the accessibility of healthcare information across borders.

In the finance sector, EXAONE's bilingual proficiency can drive enhanced customer service through multilingual chatbots and virtual assistants. These AI tools can process complex queries in various languages, making financial services more accessible to global audiences. They could also assist in automating tasks such as fraud detection, compliance monitoring, and report generation, improving operational efficiency.

In education, the bilingual AI models can play a transformative role by offering tailored educational resources and real-time language translation, helping students and teachers from different linguistic backgrounds engage more effectively. This can be particularly useful in online learning platforms, where students can interact with AI-powered tutors or access learning materials in their preferred language, ultimately improving learning outcomes and inclusivity.

By providing these capabilities, EXAONE 3.5 is positioned to lead the way in creating more integrated and accessible solutions across these critical sectors.

The Future of EXAONE and Generative AI

The release of EXAONE 3.5 marks a significant step in advancing AI research and development, particularly in the realm of generative AI. This release underscores the growing trend of developing bilingual, large-scale AI models designed to tackle complex tasks like instruction following and long-context understanding, which are crucial for improving AI's global application across various domains. EXAONE 3.5, with its ability to handle vast amounts of variables (around 300 billion), sets a new standard for AI capabilities. By focusing on both instruction-following and context understanding, it broadens the scope of AI's potential to revolutionize industries such as healthcare, customer service, and education.

In addition to its technical achievements, the bilingual nature of EXAONE 3.5 represents a major step forward in the field of natural language processing (NLP). Multilingual models are key to breaking language barriers, ensuring that AI systems can serve a global audience more effectively. The accessibility of EXAONE as an open-source model also aligns with a broader movement toward democratizing AI, allowing researchers, developers, and businesses to collaborate more easily on improving and fine-tuning these systems.

Looking ahead, EXAONE 3.5 paves the way for more scalable and efficient AI models that can be deployed in real-world scenarios, where understanding complex instructions and managing long contextual data are essential. This aligns with the growing trend toward developing AI that not only performs specific tasks well but is also capable of handling nuanced, multifaceted inputs in dynamic environments.

LG AI's EXAONE series, with its recent release of EXAONE 3.5, has significant plans for future updates and expansion, focusing on both performance enhancements and broader accessibility.

One of the key innovations in EXAONE 3.5 is the introduction of an open-source strategy. This includes releasing three models for varying purposes: an ultra-lightweight 2.4 billion parameter model for on-device use, a general-purpose 7.8 billion parameter model, and a high-performance 32 billion parameter model for specialized applications. This move is expected to foster a more open AI research ecosystem and speed up innovation across the industry.

In addition to open-sourcing, EXAONE 3.5 incorporates advanced capabilities like Retrieval-Augmented Generation (RAG), which allows the model to enhance its accuracy by pulling real-time data from the web or uploaded documents. Furthermore, the integration of multi-step reasoning helps EXAONE 3.5 to break down complex queries logically.

Looking toward future expansions, LG AI plans to develop a new Large Action Model (LAM) by 2025. This AI will be capable of autonomous planning and action, which could significantly transform the way AI agents interact with real-world applications. Additionally, LG is working on expanding enterprise uses through services like ChatEXAONE, which aims to integrate AI-driven support into daily business operations, such as document summarization, translation, and coding. The company is also prioritizing long-term collaborations with major tech players like AWS, Google Cloud, and NVIDIA to ensure the continued evolution of its AI models.

These strategic moves underline LG AI's commitment to staying at the forefront of AI technology, with a strong focus on enhancing model performance, accessibility, and practical applications in business environments.

Conclusion

EXAONE 3.5 marks a significant leap in AI development, particularly for global research and innovation. Its open-source bilingual capabilities, designed to excel in both Korean and English, set it apart as a cutting-edge model in a competitive field. EXAONE 3.5’s release strengthens LG's position as a global player, challenging established tech giants from the U.S. and China, and emphasizing South Korea’s growing prominence in AI research. The model promises to drive innovation in AI by supporting long-context understanding and improved instruction-following, which are essential for advancing fields like natural language processing (NLP), machine learning, and deep learning.

The model’s accessibility via open-source allows rapid improvements through community contributions, promoting a global ecosystem of AI researchers, developers, and businesses. By fostering collaboration, EXAONE 3.5 accelerates the development of new AI applications, offering cost-efficient solutions for enterprises and enhancing user experiences across different sectors. As the AI race intensifies, EXAONE 3.5's ability to democratize advanced AI technology positions it as a key tool for innovation on the global stage.

This strategic move also bolsters LG’s ambition to expand its presence in cloud computing and AI services, creating new avenues for growth and providing a viable alternative to AI models from companies like OpenAI, Microsoft, and Google.