EXAONE 3.5: Series of Large Language Models for Real-world Use Cases
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Abstract (arXiv)
This technical report introduces the EXAONE 3.5 instruction-tuned language models, developed and released by LG AI Research. The EXAONE 3.5 language models are offered in three configurations: 32B, 7.8B, and 2.4B. These models feature several standout capabilities: 1) exceptional instruction following capabilities in real-world scenarios, achieving the highest scores across seven benchmarks, 2) outstanding long-context comprehension, attaining the top performance in four benchmarks, and 3) competitive results compared to state-of-the-art open models of similar sizes across nine general benchmarks. The EXAONE 3.5 language models are open to anyone for research purposes and can be downloaded from https://huggingface.co/LGAI-EXAONE. For commercial use, please reach out to the official contact point of LG AI Research: [email protected].
Key facts. Cited by 53 papers in the map (data through 14 Oct 2026). Builds on 41 of its 52 references that are in the map. First posted on arXiv 6 Dec 2024.
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References · 41
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Citing papers per year: 2025: 32; 2026: 21.
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- 2025Evaluating Legal Reasoning Traces with Legal Issue Tree Rubricscited by 5
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- 2025ScholarBench: A Bilingual Benchmark for Abstraction, Comprehension, and Reasoning Evaluation in Academic Contextscited by 1
- 2025Everyday Physics in Korean Contexts: A Culturally Grounded Physical Reasoning Benchmarkcited by 1
- 2025Ko-MuSR: A Multistep Soft Reasoning Benchmark for LLMs Capable of Understanding Koreancited by 0
- 2025Soft Inductive Bias Approach via Explicit Reasoning Perspectives in Inappropriate Utterance Detection Using Large Language Modelscited by 0
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- 2026Toward Culturally Aligned LLMs through Ontology-Guided Multi-Agent Reasoning29 Jan 2026
- 2025Soft Inductive Bias Approach via Explicit Reasoning Perspectives in Inappropriate Utterance Detection Using Large Language Models9 Dec 2025
- 2025Evaluating Legal Reasoning Traces with Legal Issue Tree Rubrics30 Nov 2025
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12 most cited of 33 All 33 (JSON)
- 2026KoAGE: Do Large Language Models Know, Learn, and Coin Korean Neologisms?14 Oct 2026
- 2026Evaluating Communicative Success in Machine-Translated Conversation17 Sep 2026
- 2026Don't Repeat Yourself: Self-Supervised Fine-Tuning for Coverage16 Sep 2026
- 2026An LLM-Associated Register Shift in Korean Journal Abstracts: A Morphology-Aware Excess-Vocabulary Study, 2018-20267 Sep 2026
- 2026Scaling Creative Writing Beyond Story-Centric Data with Attribute-Guided Genre Expansion14 Aug 2026
- 2026After the Euclidean Highway: Hyperbolic Expert AI as the Next Innovation20 Jul 2026
- 2026CHERRY: Compressed Hierarchical Experts with Recurrent Representational Yield30 Jun 2026
- 2026Discovering Lexical Gaps Using Embeddings from Multilingual LLMs23 May 2026
- 2026Analyzing Error Propagation in Korean Spoken QA with ASR-LLM Cascades17 May 2026
- 2026From Relevance to Authority: Authority-aware Generative Retrieval in Web Search Engines15 Apr 2026
- 2026SCRIPT: A Subcharacter Compositional Representation Injection Module for Korean Pre-Trained Language Models14 Apr 2026
- 2026Privasis: Synthesizing the Largest "Public" Private Dataset from Scratch3 Feb 2026
12 newest of 33 All 33 (JSON)