Conducting Experiments

Once a team is formed and a proposal is in place, the core of the research work begins with conducting experiments. This phase is heavily reliant on the ability to implement and manage large-scale systems. The evaluation-focused competitions we participate in often involve datasets ranging from tens to hundreds of gigabytes, demanding efficient data processing and robust code.

The Experimental Workflow

The process begins with downloading the dataset and performing a thorough exploratory data analysis (EDA). The goal of EDA is to develop a deep understanding of the data's characteristics, including its schema, the size and nature of the train/test splits, and the statistical properties of its main features. Following this analysis, you must design your experiments, starting with a clear and simple baseline. For an information retrieval task, this might involve running a BM25 keyword search. You then implement your novel methodology, which is intended to improve upon this baseline. A key part of this stage is conducting ablation studies, where you systematically remove components from your system to isolate and quantify their individual contribution to the overall performance.

Organization and Reproducibility

As you conduct these experiments, meticulous organization of both code and data is paramount to ensure your results are reproducible. While specific organizational strategies are left to individual teams, it is essential to keep a detailed log of all experiments, their parameters, and their outcomes. This can be managed in a spreadsheet or integrated directly into your paper draft. Furthermore, you must be familiar with the standard evaluation formats required by the venue, such as the TREC-style format common in information retrieval conferences.

Team Collaboration and Project Management

Beyond the technical execution, conducting experiments successfully is a significant project management challenge. A critical task for the team is to strategically break down the work into smaller, independent components that members can tackle in parallel. This process of decomposing the problem requires constant and clear communication among all team members. Effective communication is the most crucial tool for navigating the complexities of collaborative research, ensuring that workloads are distributed effectively and the project remains on track.