Large language models can disambiguate opioid slang on social media
Social media text shows promise for monitoring trends in the opioid overdose crisis; however, the overwhelming majority of social media text is unrelated to opioids. When leveraging social media text to monitor trends in the ongoing opioid overdose crisis, a common strategy for identifying relevant content is to use a lexicon of opioid-related terms as inclusion criteria. However, many slang terms for opioids, such as "smack" or "blues," have common non-opioid meanings, making them ambiguous. The advanced textual reasoning capability of large language models (LLMs) presents an opportunity to disambiguate these slang terms at scale. We present three tasks on which to evaluate four state-of...
| Authors | Kristy A. Carpenter, Issah A. Samori, Mathew V. Kiang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 11 March 2026 |
| Last updated at source | 11 March 2026 |
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