arXiv subjects

Alexandra Mayn

Publications and source records attributed to Alexandra Mayn.

3 recordsLinked to original sources

arXiv2026

When Contextual Inference Fails: Cancelability in Interactive Instruction Following

We investigate the separation of literal interpretation from contextual inference in a collaborative block-building tasks, where an agent must resolve underspecified instructions using context. We adapt an existing two-speaker psycholinguistic paradigm into an interactive benchmark called Build What I Mean (BWIM). This setup contrasts a pragmatically cooperative speaker with one who is only literally reliable. In BWIM, models face underspecified instructions and must choose between making a contextual inference or requesting clarification at a small communication cost. Evaluating several state-of-the-art LLMs, we find a clear dissociation between judgment and action. Although models successfully detect speaker unreliability in explicit confidence ratings, they fail to leverage this awareness when taking action. Instead of deploying efficient clarification strategies, models default to suboptimal behaviors. These include partner-blind over-clarification and question-averse guessing under uncertainty. BWIM provides a controlled environment to evaluate online partner adaptation and contextual reasoning in interactive settings.

cs.CL↗

arXiv2022

Evaluating Automatic Difficulty Estimation of Logic Formalization Exercises

Teaching logic effectively requires an understanding of the factors which cause logic students to struggle. Formalization exercises, which require the student to produce a formula corresponding to the natural language sentence, are a good candidate for scrutiny since they tap into the students' understanding of various aspects of logic. We correlate the difficulty of formalization exercises predicted by a previously proposed difficulty estimation algorithm with two empirical difficulty measures on the Grade Grinder corpus, which contains student solutions to FOL exercises. We obtain a moderate correlation with both measures, suggesting that the said algorithm indeed taps into important sources of difficulty but leaves a fair amount of variance uncaptured. We conduct an error analysis, closely examining exercises which were misclassified, with the aim of identifying additional sources of difficulty. We identify three additional factors which emerge from the difficulty analysis, namely predicate complexity, pragmatic factors and typicality of the exercises, and discuss the implications of automated difficulty estimation for logic teaching and explainable AI.

cs.LO↗

arXiv2021

A conditional independence test for causality in econometrics

The Y-test is a useful tool for detecting missing confounders in the context of a multivariate regression.However, it is rarely used in practice since it requires identifying multiple conditionally independent instruments, which is often impossible. We propose a heuristic test which relaxes the independence requirement. We then show how to apply this heuristic test on a price-demand and a firm loan-productivity problem. We conclude that the test is informative when the variables are linearly related with Gaussian additive noise, but it can be misleading in other contexts. Still, we believe that the test can be a useful concept for falsifying a proposed control set.

stat.ME↗