Description
GPT-4 is a transformer based model pre-trained to predict the next token in a document.
Papers Using This Method
Foundation Models for Logistics: Toward Certifiable, Conversational Planning Interfaces2025-07-15An Empirical Evaluation of AI-Powered Non-Player Characters' Perceived Realism and Performance in Virtual Reality Environments2025-07-14Learning from Synthetic Labs: Language Models as Auction Participants2025-07-12Agent KB: Leveraging Cross-Domain Experience for Agentic Problem Solving2025-07-08Large Language Models Don't Make Sense of Word Problems. A Scoping Review from a Mathematics Education Perspective2025-06-30Probing AI Safety with Source Code2025-06-25Lost in Translation? Converting RegExes for Log Parsing into Dynatrace Pattern Language2025-06-24SWE-SQL: Illuminating LLM Pathways to Solve User SQL Issues in Real-World Applications2025-06-23Security Assessment of DeepSeek and GPT Series Models against Jailbreak Attacks2025-06-23Leveraging LLMs to Assess Tutor Moves in Real-Life Dialogues: A Feasibility Study2025-06-20UProp: Investigating the Uncertainty Propagation of LLMs in Multi-Step Agentic Decision-Making2025-06-20I Know Which LLM Wrote Your Code Last Summer: LLM generated Code Stylometry for Authorship Attribution2025-06-18DiscoSG: Towards Discourse-Level Text Scene Graph Parsing through Iterative Graph Refinement2025-06-18ImpliRet: Benchmarking the Implicit Fact Retrieval Challenge2025-06-17Scaling Intelligence: Designing Data Centers for Next-Gen Language Models2025-06-17Alphabet Index Mapping: Jailbreaking LLMs through Semantic Dissimilarity2025-06-15Evaluating Cell Type Inference in Vision Language Models Under Varying Visual Context2025-06-15Language Models Enable Data-Augmented Synthesis Planning for Inorganic Materials2025-06-14Identifying Helpful Context for LLM-based Vulnerability Repair: A Preliminary Study2025-06-13Leveraging GPT-4 for Vulnerability-Witnessing Unit Test Generation2025-06-13