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What's in a Name? Morphological Shortcuts by LLMs in Pharmacology
Kaijie Mo, Thomas Yang, Chantal Shaib +6
The morphological form of a word can often give cues to its meaning, but purely relying on these mappings can lead to overgeneralization in high-stakes domains. In the medical doma…
Faithfulness vs. Safety: Evaluating LLM Behavior Under Counterfactual Medical Evidence
Kaijie Mo, Siddhartha Venkatayogi, Chantal Shaib +4
In high-stakes domains like medicine, it may be generally desirable for models to faithfully adhere to the context provided. But what happens if the context does not align with mod…
Learning the Wrong Lessons: Syntactic-Domain Spurious Correlations in Language Models
Chantal Shaib, Vinith M. Suriyakumar, Levent Sagun +2
For an LLM to correctly respond to an instruction it must understand both the semantics and the domain (i.e., subject area) of a given task-instruction pair. However, syntax can al…
Measuring AI "Slop" in Text
Chantal Shaib, Tuhin Chakrabarty, Diego Garcia-Olano +1
AI "slop" is an increasingly popular term used to describe low-quality AI-generated text, but there is currently no agreed upon definition of this term nor a means to measure its o…
Measuring Lexical Diversity of Synthetic Data Generated through Fine-Grained Persona Prompting
Gauri Kambhatla, Chantal Shaib, Venkata Govindarajan
Fine-grained personas have recently been used for generating 'diverse' synthetic data for pre-training and supervised fine-tuning of Large Language Models (LLMs). In this work, we…
Who Taught You That? Tracing Teachers in Model Distillation
Somin Wadhwa, Chantal Shaib, Silvio Amir +1
Model distillation -- using outputs from a large teacher model to teach a small student model -- is a practical means of creating efficient models for a particular task. We ask: Ca…