mlsanityData to 14 Oct 2026

CoLa: Learning to Interactively Collaborate with Large Language Models

arXiv 2504.02965 ↗First posted 3 Apr 2025cs.CL cs.AI cs.LGcited by 0

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Abstract (arXiv)

LLMs' remarkable ability to tackle a wide range of language tasks opened new opportunities for collaborative human-AI problem solving. LLMs can amplify human capabilities by applying their intuitions and reasoning strategies at scale. We explore whether human guides can be simulated, by generalizing from human demonstrations of guiding an AI system to solve complex language problems. We introduce CoLa, a novel self-guided learning paradigm for training automated textitguides and evaluate it on two QA datasets, a puzzle-solving task, and a constrained text generation task. Our empirical results show that CoLa consistently outperforms competitive approaches across all domains. Moreover, a small-sized trained guide outperforms a strong model like GPT-4 when acting as a guide. We compare the strategies employed by humans and automated guides by conducting a human study on a QA dataset. We show that automated guides outperform humans by adapting their strategies to reasoners' capabilities and conduct qualitative analyses highlighting distinct differences in guiding strategies.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 35 of its 46 references that are in the map. First posted on arXiv 3 Apr 2025.

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References · 35

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23 more
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