arXiv subjects

Catarina Belem

Publications and source records attributed to Catarina Belem.

3 recordsLinked to original sources

arXiv2026

The Impact of AI Usage and Informativeness on Skill Development in Logical Reasoning

Artificial intelligence (AI) is being increasingly integrated into human problem-solving, yet its effects on individual skill development remain unclear. We examine how both AI usage and informativeness can shape learning in the context of a controlled logical reasoning task with on-demand access to AI assistance. We find that greater AI usage is associated with weaker skill development: heavy AI users underperform relative to comparable peers, whereas light AI users perform similarly to matched users who do not use AI. We also find in our study that these patterns are mediated by AI informativeness. Low-information AI neither improves immediate performance nor preserves performance after AI assistance is removed, and is linked to weaker learning overall. On the other hand, high-information AI was found to improve short-run performance without reducing post-AI outcomes on average in our experiments, but with heterogeneous effects. Our findings in general suggest that AI can, depending on context, either complement human skill development by amplifying independent reasoning or can act as a substitute that undermines such reasoning, with the implication that regulating AI access and usage will be important for promoting skill development in the presence of AI assistance.

cs.AI↗

arXiv2025

Generalization of Fine-Tuned Uncertainty Communication and Metacognition in Large Language Models

Background. Large language models are increasingly used in settings where confident but incorrect answers can mislead users. Reliable uncertainty communication requires a form of metacognition: monitoring when one's own answers are likely to be correct. Yet models' stated confidence is often poorly aligned with answer correctness. We test whether supervised fine-tuning improves uncertainty communication and whether gains transfer across domains and task formats. Methods. We fine-tuned two models on general knowledge, mathematics, and open-ended trivia questions. We evaluated single-question confidence estimation, in which the model reports numeric confidence for one answer, and pairwise confidence comparison, in which it chooses which of two questions it is more likely to answer correctly. We tested held-out questions from training domains and new medical, legal, and truthfulness benchmarks. We assessed calibration, discrimination, and answer accuracy before and after fine-tuning. Results. Here we show that fine-tuning improves alignment between stated confidence and observed accuracy and increases the model's ability to assign higher confidence to correct than to incorrect answers. Gains occur within training domains and, to a lesser extent, in new domains. However, single-task training does not reliably transfer between single-question confidence estimation and pairwise confidence comparison. Multitask fine-tuning produces broader gains in the models and tasks studied here. Conclusions. Uncertainty communication in large language models is trainable, but transfer across metacognitive tasks is limited. Joint training on multiple confidence tasks may support broader generalization, although further tests across model families and metacognitive tasks are needed.

cs.CL↗

arXiv2024

What Large Language Models Know and What People Think They Know

As artificial intelligence (AI) systems, particularly large language models (LLMs), become increasingly integrated into decision-making processes, the ability to trust their outputs is crucial. To earn human trust, LLMs must be well calibrated such that they can accurately assess and communicate the likelihood of their predictions being correct. Whereas recent work has focused on LLMs' internal confidence, less is understood about how effectively they convey uncertainty to users. Here we explore the calibration gap, which refers to the difference between human confidence in LLM-generated answers and the models' actual confidence, and the discrimination gap, which reflects how well humans and models can distinguish between correct and incorrect answers. Our experiments with multiple-choice and short-answer questions reveal that users tend to overestimate the accuracy of LLM responses when provided with default explanations. Moreover, longer explanations increased user confidence, even when the extra length did not improve answer accuracy. By adjusting LLM explanations to better reflect the models' internal confidence, both the calibration gap and the discrimination gap narrowed, significantly improving user perception of LLM accuracy. These findings underscore the importance of accurate uncertainty communication and highlight the effect of explanation length in influencing user trust in AI-assisted decision-making environments. Code and Data can be found at https://osf.io/y7pr6/ . Journal publication can be found on Nature Machine Intelligence at https://www.nature.com/articles/s42256-024-00976-7 .

cs.LG↗