Research software is not always meant to reach production-grade quality. The same requirements, for instance, regarding performance, security, or reliability that are imposed on professional software do not necessarily apply in the lab. However, recent years have seen an increased interest to bring cutting-edge research results into production. Specialized natural language processing, such as NSLP, is no exception. In this paper, we discuss three real-life challenges in scientific publishing as they relate to NSLP, and also highlight the potential NSLP has to offer in overcoming these challenges. Specifically, we identify issues related to the elicitation of metadata – particularly with respect to what we term the /metadata externality problem –, privacy and data protection laws, and software reliability. This is not a research paper; rather than introducing novel research results, our intention is to contribute to the academic discourse in the NSLP community by providing the perspective of a potential end-user.

Firearm violence is a pressing public health issue, yet research into survivors’ lived experiences remains underfunded and difficult to scale. Qualitative research, including in-depth interviews, is a valuable tool for understanding the personal and societal consequences of community firearm violence and designing effective interventions. However, manually analyzing these narratives through thematic analysis and inductive coding is time-consuming and labor-intensive. Recent advancements in large language models (LLMs) have opened the door to automating this process, though concerns remain about whether these models can accurately and ethically capture the experiences of vulnerable populations. In this study, we assess the use of open-source LLMs to inductively code interviews with 21 Black men who have survived community firearm violence. Our results demonstrate that while some configurations of LLMs can identify important codes, overall relevance remains low and is highly sensitive to data processing. Furthermore, LLM guardrails lead to substantial narrative erasure. These findings highlight both the potential and limitations of LLM-assisted qualitative coding and underscore the ethical challenges of applying AI in research involving marginalized communities.

Idioms are unlike most phrases in two important ways. First, words in an idiom have non-canonical meanings. Second, the non-canonical meanings of words in an idiom are contingent on the presence of other words in the idiom. Linguistic theories differ on whether these properties depend on one another, as well as whether special theoretical machinery is needed to accommodate idioms. We define two measures that correspond to the properties above, and we show that idioms fall at the expected intersection of the two dimensions, but that the dimensions themselves are not correlated. Our results suggest that introducing special machinery to handle idioms may not be warranted.