For SemEval 2025 Task 8, addressing tabular data question answering, we introduce a novel few-shot prompting system that guides large language models (LLMs) to generate Python code representing the reasoning process. Our system automatically creates a library of exemplar code snippets from training data, which are then used for few-shot prompting. Crucially, we incorporate a selection prompt to choose the best candidate code from multiple LLM-generated options, improving robustness and accuracy. Our system achieved competitive results, ranking 17th in the Open Model track and 25th overall. Ablation studies demonstrate the effectiveness of our exemplar generation and code selection strategies. We conclude with a discussion of limitations and promising avenues for future research.

Lay summarization of biomedical research articles is a challenging problem due to their use of technical terms and background knowledge requirements, despite the potential benefits of these research articles to the public. We worked on this problem as participating in BioLaySumm 2024. We experimented with various fine-tuning approaches to generate better lay summaries for biomedical research articles. After several experiments, we built a LoRA model with unsupervised fine-tuning based on the abstracts of the given articles, followed by a post-processing unit to take off repeated sentences. Our model was ranked 3rd overall in the BioLaySumm 2024 leaderboard. We analyzed the different approaches we experimented with and suggested several ideas to improve our model further.

Participating in SemEval 2024 Task 2, we built a three-module system to predict entailment labels for NLI4CT, which consists of a sequence of the query generation module, the query answering module, and the aggregation module. We fine-tuned or prompted each module with the intermediate labels we generated with LLMs, and we optimized the combinations of different modules through experiments. Our system is ranked 19th ~ 24th in the SemEval 2024 Task 2 leaderboard in different metrics. We made several interesting observations regarding the correlation between different metrics and the sensitivity of our system on the aggregation module. We performed the error analysis on our system which can potentially help to improve our system further.