mlsanityData to 14 Oct 2026

IPEval: A Bilingual Intellectual Property Agency Consultation Evaluation Benchmark for Large Language Models

arXiv 2406.12386 ↗First posted 18 Jun 2024cs.CLcited by 3

On the map: LLM reasoning → Reasoning for agents, planning and code

Abstract (arXiv)

The rapid development of Large Language Models (LLMs) in vertical domains, including intellectual property (IP), lacks a specific evaluation benchmark for assessing their understanding, application, and reasoning abilities. To fill this gap, we introduce IPEval, the first evaluation benchmark tailored for IP agency and consulting tasks. IPEval comprises 2657 multiple-choice questions across four major dimensions: creation, application, protection, and management of IP. These questions span patent rights (inventions, utility models, designs), trademarks, copyrights, trade secrets, and other related laws. Evaluation methods include zero-shot, 5-few-shot, and Chain of Thought (CoT) for seven LLM types, predominantly in English or Chinese. Results show superior English performance by models like GPT series and Qwen series, while Chinese-centric LLMs excel in Chinese tests, albeit specialized IP LLMs lag behind general-purpose ones. Regional and temporal aspects of IP underscore the need for LLMs to grasp legal nuances and evolving laws. IPEval aims to accurately gauge LLM capabilities in IP and spur development of specialized models. Website: urlhttps://ipeval.github.io/

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning. Builds on 13 of its 19 references that are in the map. First posted on arXiv 18 Jun 2024.

Builds on

13 of 19 references.

References · 13

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2023GPT-4 Technical Reportcited by 18,396
  3. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  4. 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,780
  5. 2023Qwen Technical Reportcited by 3,344
  6. 2020What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Examscited by 1,356
  7. 2021GLM: General Language Model Pretraining with Autoregressive Blank Infillingcited by 1,031
  8. 2022GLM-130B: An Open Bilingual Pre-trained Modelcited by 843
  9. 2023Baichuan 2: Open Large-scale Language Modelscited by 670
  10. 2023LawBench: Benchmarking Legal Knowledge of Large Language Modelscited by 117
  11. 2022The Harvard USPTO Patent Dataset: A Large-Scale, Well-Structured, and Multi-Purpose Corpus of Patent Applicationscited by 33
  12. 2024E-EVAL: A Comprehensive Chinese K-12 Education Evaluation Benchmark for Large Language Modelscited by 13
1 more
  1. 2024MoZIP: A Multilingual Benchmark to Evaluate Large Language Models in Intellectual Propertycited by 7

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 0; 2026: 2.

3 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2025IPBench: Benchmarking the Knowledge of Large Language Models in Intellectual Property
  2. 2024PatentGPT: A Large Language Model for Intellectual Property
  3. 2025PATENTWRITER: A Benchmarking Study for Patent Drafting with LLMs
  4. 2024OpenEval: Benchmarking Chinese LLMs across Capability, Alignment and Safety
  5. 2026PILOT-Bench: A Benchmark for Legal Reasoning in the Patent Domain with IRAC-Aligned Classification Tasks
  6. 2026Pat-DEVAL: Chain-of-Legal-Thought Evaluation for Patent Description
  7. 2025QualBench: Benchmarking Chinese LLMs with Localized Professional Qualifications for Vertical Domain Evaluation
  8. 2025Retrieval-Augmented Generation Systems for Intellectual Property via Synthetic Multi-Angle Fine-tuning