Surprisal Minimisation over Goal-directed Alternatives Predicts Production Choice in Dialogue
Researchers model dialogue production as probabilistic choice among contextual alternatives, using information theory to distinguish between utterances that serve a fixed communicative goal versus those merely plausible in context. By generating alternative sets via language models and analyzing real dialogue, they show that surprisal minimization relative to goal-directed alternatives outperforms competing theories like uniform information density. This work refines how we understand speaker behavior in LLM-based dialogue systems and offers a principled framework for predicting which utterance an agent will select, with implications for more human-like generation strategies.
Modelwire context
ExplainerThe paper's key contribution is showing that speakers don't simply minimize surprisal in context (a simpler theory), but specifically minimize surprisal relative to utterances that accomplish the same communicative goal. This distinction matters because it means dialogue agents need to model intent, not just probability.
This connects directly to the memory and orchestration work from early May. The MemCoE paper (Learning How and What to Memorize) and RunAgent both grapple with how LLM agents maintain coherent intent across multi-turn interactions. This surprisal-minimization framework offers a principled way to predict which utterance an agent will select given a fixed goal, which is precisely what orchestration layers need when routing between candidate responses. The Structure Liberates paper similarly emphasizes that constrained cognitive scaffolding improves both fidelity and output quality; here, goal-directed constraints on the utterance space improve predictability of speaker behavior.
If teams implementing dialogue agents in production systems (like customer service or multi-turn reasoning tasks) adopt this goal-directed surprisal framework and report measurable improvements in response consistency or human preference scores compared to standard LLM sampling, that signals the theory has practical teeth. Watch whether papers on dialogue-based agents published in the next two quarters cite this work as a baseline for utterance selection.
Coverage we drew on
This analysis is generated by Modelwire’s editorial layer from our archive and the summary above. It is not a substitute for the original reporting. How we write it.
MentionsLanguage models
Modelwire Editorial
This synthesis and analysis was prepared by the Modelwire editorial team. We use advanced language models to read, ground, and connect the day’s most significant AI developments, providing original strategic context that helps practitioners and leaders stay ahead of the frontier.
Modelwire summarizes, we don’t republish. The full content lives on arxiv.org. If you’re a publisher and want a different summarization policy for your work, see our takedown page.