Structured prompts can steer LLMs to flag certain unsupported claims and ambiguous pronouns, but performance varies sharply by model, context, and the syntactic role of the target, and the single test case was also the development case.
Writing Tools: Looking Back to Look Ahead
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abstract
Research on writing tools started with the increased availability of computers in the 1970s. After a first phase addressing the needs of programmers and data scientists, research in the late 1980s started to focus on writing-specific needs. Several projects aimed at supporting writers and letting them concentrate on the creative aspects of writing by having the writing tool take care of the mundane aspects using NLP techniques. Due to technical limitations at that time the projects failed and research in this area stopped. However, today's computing power and NLP resources make the ideas from these projects technically feasible; in fact, we see projects explicitly continuing from where abandoned projects stopped, and we see new applications integrating NLP resources without making references to those old projects. To design intelligent writing assistants with the possibilities offered by today's technology, we should re-examine the goals and lessons learned from previous projects to define the important dimensions to be considered.
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AI-Facilitated Analysis of Abstracts and Conclusions: Flagging Unsubstantiated Claims and Ambiguous Pronouns
Structured prompts can steer LLMs to flag certain unsupported claims and ambiguous pronouns, but performance varies sharply by model, context, and the syntactic role of the target, and the single test case was also the development case.