Thesis Proposal: Intentional Inference for Insight Generation

Abstract

Large language models (LLMs) show strong capabilities in natural language generation (NLG) and have been applied to translate complex structured data into human-readable insights. While these models excel at surface-level fluency, they remain unreliable as they produce factually inaccurate outputs and struggle with consistent logical inference beyond surface-level patterns. Moreover, they often lack a clear sense of relevance and produce shallow or un-informative insights. This proposal argues that a key source of these limitations is task underspecification, which requires models to make implicit assumptions about missing context. We investigate how such underspecification leads to unintentional assumptions and how these affect faithfulness and evaluation. We examine how models can identify missing premises and surface multiple plausible interpretations to make evaluation more rigorous. We also explore how to improve reasoning to enable deeper inferences, focusing on code generation and qualitative reasoning. Finally, we will evaluate how the underlying assumptions and depth of inference influence the perceived interestingness of the insights. By shifting focus from surface-level generation to assumption-aware deeper inferences, this work aims to improve reliability, interpretability, and user controllability in NLG.