https://dasya.itu.dk/tags/llms/ Recent content in LLMs on DASYA Hugo -- gohugo.io en-us © DASYA, IT University of Copenhagen Wed, 05 Aug 2026 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/current/pito-llm-agents/ Wed, 05 Aug 2026 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/current/pito-llm-agents/ This project would investigate the storage access patterns of LLM agents using open-source state-of-the-art inference frameworks such as vLLM and SGLang and workload traces such as the ones released by Mooncake, Alibaba, Swiss AI Initiative, etc. Depending on the size and duration of the project, it can also propose or implement optimizations for the storage access. This project would be suitable as a standalone project, a research project with an MSc thesis followup, or BSc thesis. https://dasya.itu.dk/for-students/proposals/current/pito-llm-kvcache/ Wed, 05 Aug 2026 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/current/pito-llm-kvcache/ This project would investigate key-value(KV)-cache offloading in large language model (LLM) inference engines, with particular emphasis on data placement and storage efficiency. First, the project would use an emulator such as FEMU to characterize workload access patterns, including hot-key distributions, write intensity, and write amplification factor (WAF). Then, we would evaluate the potential of Flexible Data Placement (FDP) for improving data placement decisions on SSDs for KV-cache accesses. The project will use open source state-of-the-art KV-cache types such as LMCache or the ones implemented by the inference frameworks such as vLLM and SGLang. https://dasya.itu.dk/for-students/proposals/current/mh-llm-rng-personas/ Fri, 24 Jul 2026 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/current/mh-llm-rng-personas/ BSc Thesis. This project builds on a previous master’s thesis, Prompting Luck: Bias and Manipulability in LLM-Generated Randomness, that explored replacing traditional random number generators (RNG) in games with large language models (LLMs). The idea is that an LLM can produce “intelligent” random numbers, for example the outcome of a slot machine spin, that take context into account rather than being drawn from a uniform distribution. The goal of this project is to experimentally validate the findings of that thesis by building an actual game, connecting it to an LLM, and putting the idea to the test with real players. https://dasya.itu.dk/for-students/proposals/archive/mh-llm-vs-rng/ Thu, 20 Nov 2025 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/mh-llm-vs-rng/ BSc Thesis / MSc Thesis. The goal of this project is to explore the feasibility and impact of replacing traditional random number generators (RNG) in games with large language models (LLMs). For example, in a slot machine game, the outcome is typically determined by a random number generated each time the “spin” button is pressed. In this project, students will investigate using an LLM to generate these numbers instead. Research questions include: https://dasya.itu.dk/for-students/proposals/archive/mh-generated-dataset/ Wed, 02 Jul 2025 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/mh-generated-dataset/ (MSc Research Project / MSc Thesis) The goal of this project is to research how AI and large language models generate datasets. Research questions include: Where does the generated data come from? Are sources available on the internet or can they be found? What biases exist in the generated data? And how much of the data is simply wrong? Generated datasets are used in many fields in practice, including journalism (e. https://dasya.itu.dk/for-students/proposals/current/zk-kges/ Wed, 28 May 2025 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/current/zk-kges/ Knowledge graphs (KGs) are extensively used in many application domains, such as search engines, product recommendation, and bioinformatics. Knowledge graph completion (a.k.a.~link prediction), i.e.,~the task of inferring missing information from knowledge graphs, is a widely used task in the above applications. This project will investigate how to loosely-couple the data-driven power of knowledge graph embeddings with domain-specific reasoning stemming from experts or reasoning systems. In this way, we not only enhance the prediction accuracy with domain knowledge that may not be included in the input knowledge graph but also allow users to plugin their own knowledge graph embedding and reasoning method. https://dasya.itu.dk/for-students/proposals/current/zk-apachewayang-mcp/ Wed, 28 May 2025 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/current/zk-apachewayang-mcp/ Are you interested in working with a big data open source project and AI? You are welcome to conduct your thesis/project in the context of Apache Wayang. Apache Wayang is the first cross-platform framework that allows users to specify their task/query in a system-agnostic manner and Wayang will determine which is the best system(s) to execute this task with the goal of optimizing performance. For a general overview check this paper.