The Curious Case of Factual (Mis)Alignment between LLMs' Short- and
  Long-Form Answers
Authors: name: Saad Obaid ul Islam, name: Anne Lauscher, name: Goran Glavaš
Organization: WüNLP

Large language models (LLMs) can correctly answer "When was Einstein born?"
yet fail to provide the same date when writing about Einstein's life revealing
a fundamental inconsistency in how models access factual knowledge across task
complexities. While models display impressive accuracy on factual
question-answering benchmarks, the reliability gap between simple and complex
queries remains poorly understood, eroding their trustworthiness. In this work,
we introduce Short-Long Form Alignment for Factual Question Answering (SLAQ), a
controlled evaluation framework that compares LLMs' answers to the same factual
questions asked (a) in isolation (short) vs. (b) integrated into complex
queries (long). Looking at 16 LLMs across 600 queries, we find a systematic
misalignment of answers to the corresponding short and long queries. We further
uncover position-dependent accuracy loss and momentum effects where consecutive
correct or incorrect answers create self-reinforcing patterns. Through
mechanistic analysis, we find that aligned facts activate overlapping model
internals, and that metrics based on mechanistic similarity can predict
short-long answer alignment with up to 78% accuracy. Our work establishes
factual consistency over query complexity as an important aspect of LLMs'
trustworthiness and challenges current evaluation practices, which implicitly
assume that good performance for simple factual queries implies reliability in
more complex knowledge-seeking tasks too.