Venture capital term sheets are critical instruments for structuring early-stage investment agreements, yet their manual preparation remains time-consuming and dependent on specialized legal expertise. Recent advances in natural language processing (NLP), particularly large language models (LLMs), have opened new opportunities for automating the interpretation of complex financial and legal documents. This paper frames the challenge of term sheet drafting as an information extraction problem, aiming to generate standardized term sheets automatically by extracting and structuring data from unstructured contract texts. To support this process, we constructed a domain-specific term dictionary derived from real investment agreements obtained through the U.S. Securities and Exchange Commission’s (SEC) Electronic Data Gathering, Analysis, and Retrieval (EDGAR) system. The dictionary organizes terms under a set of umbrella categories and provides clear operational definitions for each element, enabling consistent and interpretable extraction. Building on this foundation, we developed an intelligent LLM empowered agent specialized in financial contracts that can extract key deal terms with contextual awareness. The system was evaluated on a corpus of 59 contracts sourced from EDGAR, benchmarking it against expert annotations across multiple contractual categories. Though the model had difficulty identifying context-dependent clauses in certain categories, it achieved an overall correspondence rate of 86.92% with human evaluations. These findings underscore both the promise and the limitations of scalable automation in venture-financing document analysis, suggesting that further domain calibration and fine-tuning are necessary for optimal accuracy. Overall, the results highlight the potential of LLM-driven systems to structure and surface relevant information, streamline due diligence, and support the development of AI-assisted tools for contract intelligence and venture-finance decision-making.
Sartipi et al. (Thu,) studied this question.