Papers by Mike Conway
FigSIM: A Dataset for Fine-grained Suicide Severity and Figurative Language in Suicide Memes (2026.findings-acl)
Copied to clipboard
| Challenge: | Suicide memes are increasingly common on social media, yet remain poorly understood and potentially harmful. |
| Approach: | They propose a dataset designed for fine-grained analysis of suicide memes and benchmark 16 models for figurative language, suicide severity, and content detection. |
| Outcome: | The proposed model outperforms existing models on figurative language, suicide severity, and suicide-related content detection tasks. |
Towards semantic reliable clinical QA: Query pipeline optimization for cancer patient question answering systems (2026.findings-acl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) are promising for medical Question-Answering but suffer from hallucinations that jeopardize patient safety. |
| Approach: | They propose a three-level controllable metadata-aware framework optimized for Cancer Patient QA (CPQA) they propose combining semantic retrieval with real-time Boolean search to overcome metadata blindness. |
| Outcome: | The proposed framework improves the answer accuracy of Claude-3-haiku by 5.24% over chain-of-thought prompting and about 3% over a naive RAG setup. |
Generating Mental Health Transcripts with SAPE (Spanish Adaptive Prompt Engineering) (2024.naacl-long)
Copied to clipboard
| Challenge: | Large language models can generate synthetic data resembling real-world data, but their generative performance depends on the quality of the prompt used to instruct the model. |
| Approach: | They propose a Spanish Adaptive Prompt Engineering method that uses genetic algorithms to generate and select prompts that resemble real-world data. |
| Outcome: | The proposed method produces Spanish therapy transcripts that more closely resemble authentic therapy transcript compared to other prompt engineering techniques that are based on Reflexion and Chain-of-Thought. |