Towards Open-Ended Discovery for Low-Resource NLP (2025)

Abstract: Natural Language Processing (NLP) for low-resource languages remains fundamentally constrained by the lack of textual corpora, standardized orthographies, and scalable annotation pipelines. While recent advances in large language models have improved cross-lingual transfer, they remain inaccessible to underrepresented communities due to their reliance on massive, pre-collected data and centralized infrastructure. In this position paper, we argue for a paradigm shift toward open-ended, interactive language discovery, where AI systems learn new languages dynamically through dialogue rather than static datasets. We contend that the future of language technology, particularly for low-resource and under-documented languages, must move beyond static data collection pipelines toward interactive, uncertainty-driven discovery, where learning emerges dynamically from human-machine collaboration instead of being limited to pre-existing datasets. We propose a framework grounded in joint human-machine uncertainty, combining epistemic uncertainty from the model with hesitation cues and confidence signals from human speakers to guide interaction, query selection, and memory retention. This paper is a call to action: we advocate a rethinking of how AI engages with human knowledge in under-documented languages, moving from extractive data collection toward participatory, co-adaptive learning processes that respect and empower communities while discovering and preserving the world's linguistic diversity. This vision aligns with principles of human-centered AI, emphasizing interactive, cooperative model building between AI systems and speakers.

## introduction the recent progress in natural language processing (nlp) has been largely shaped by a data-driven paradigm. foundation models, built on large-scale internet corpora and empowered by scaling laws, have unlocked impressive generalization across tasks and languages @xcite @xcite . however, this trajectory has come at a cost: the assumption that performance improves with ever more data and compute has made cutting-edge research increasingly inaccessible, especially to researchers and communities in the global south @xcite . despite efforts to democratize nlp, a stark imbalance persists. african languages, which make up over 30% of the world's linguistic diversity, account for less than 1% of nlp research output @xcite . these languages typically lack large-scale text corpora, parallel datasets, and standardized annotation practices. transfer learning, active learning, self-supervised and semisupervised learning, have all been proposed to address this data scarcity @xcite @xcite @xcite , but even these methods depend on the availability of some unlabeled or previously seen language data. in environments where data is extremely scarce or non-digitized, such assumptions break down. moreover, while recent large language models (llms) have demonstrated impressive crosslingual abilities, their success is closely tied to data scale, computational resources, and increasingly centralized infrastructure. as scaling laws plateau and operational costs rise, the current paradigm risks becoming both unsustainable and exclusive, limiting participation from underrepresented communities and preventing scalable solutions for the languages that need them most @xcite @xcite . we argue that nlp must now evolve beyond static, data-hungry training regimes. inspired by recent work in open-ended discovery and selfimproving ai @xcite , we propose a shift toward interactive, uncertainty-driven language learning. in our vision, ai systems learn languages not from vast corpora, but through natural dialogue, identifying gaps in their understanding, asking questions, and incorporating feedback in real time. imagine an ai system that only understands english, but receives a human input in fon @xcite @xcite @xcite . instead of guessing or ignoring it, the system responds: "i do not recognize this language. could you help me understand it?" from this first exchange, it starts acquiring the new linguistic concepts interactively. over time, through repeated exposure and correction, the system transitions from total ignorance to conversational fluency in the new language. this vision shifts the emphasis from training on what we have to learning from what we do not yet understand, as humans do. in this position paper, we explore the technical and conceptual foundations for such systems. we argue that open-ended language learning, grounded in epistemic uncertainty, dialogue, and human-in-the-loop adaptation, represents a scalable and inclusive path forward for low-resource nlp, especially in contexts where static data is not available, representative, or sufficient. we also outline a set of open challenges that arise from this vision, including the need for reliable uncertainty estimation, continuous learning mechanisms, and equitable access to interaction data. we discuss both the promise and the risks of this approach, including the question of whether such systems can acquire meaningful language competence without sufficient exposure or human feedback, and what architectures, incentives, or evaluation schemes would be required to support them. ## proposed approach our proposed framework enables ai systems to acquire language competence through open-ended, interactive learning. this process is illustrated in figure 1 through interactions between a human and the ai system (agent). rather than training on large static corpora, the system learns by engaging with users in real time, identifying gaps in its knowledge, soliciting clarification, and integrating feedback. the methodology consists of three core components: (1) modeling interactional uncertainty, (2) language acquisition via feedback, and (3) continual learning from dialogic exposure. ## opportunities and challenges our proposed framework for open-ended language discovery leverages joint human-machine uncertainty to guide interaction, query selection, and memory retention. while the approach introduces a novel paradigm for low-resource language acquisition, its success and limitations stem directly from the mechanisms we designed. unlike conventional nlp pipelines that rely on static, curated datasets and post-hoc analysis, this framework is designed for real-time, adaptive interaction. it emphasizes uncertainty-driven decision-making, enabling language acquisition to progress even when large corpora, standardized orthographies, or expert annotators are unavailable. ## conclusion this paper outlines a vision for open-ended language discovery based on joint human-machine uncertainty. we argue that future nlp systems, particularly for low-resource languages, must move beyond static data pipelines and toward interactive, participatory approaches that adapt to sparse, uncertain, and heterogeneous feedback. while many technical and sociotechnical challenges remain, this is not merely a research proposal but an ideological stance: language technology should be cocreated with speakers. we position this work as a challenge to current practices that treat language as extractable data, advocating instead for ai systems that become collaborative participants in language preservation and revitalization. we call on the nlp and hci research communities to develop methods, tools, and evaluation practices that support co-adaptive language learning systems, opening new pathways for linguistic documentation, preservation, and empowerment in the digital age. this position paper advocates for a paradigm shift: from building models that passively learn from existing data to designing systems that actively learn with people in real time, fostering respectful, humancentered ai for linguistic diversity.