Augmenting large language models (LLMs) with external context significantly improves their performance in natural language processing (NLP) tasks. However, LLMs struggle to answer queries reliably when the provided context lacks information, often resorting to ungrounded speculation or internal knowledge. Groundedness, or the ability to generate responses strictly supported by the context, is essential for ensuring factual consistency and trustworthiness. This study focuses on detecting whether a given query is grounded in a document supplied in context before the computationally expensive answer generation by LLMs. This will potentially reduce both inference time and resource consumption. We show that lightweight, task-specific encoder models such as RoBERTa, fine-tuned on curated datasets, can achieve accuracy comparable to state-of-the-art LLMs such as Llama3 8B and GPT4 in groundedness detection while reducing inference latency by orders of magnitude.

Small Encoders Can Rival Large Decoders in Detecting Groundedness

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### Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

Welcome to the 2025 meeting of the Nations of the Americas Chapter of the Association for Computational Linguistics! I am proud to help organize the first NAACL conference to carry the new name of our organization, one that emphasizes inclusion for all of the Americas. I am also pleased to welcome you to Albuquerque, New Mexico, a state whose unique blend of cultural influences will make for an excellent backdrop for NAACL 2025, especially with this year’s special theme on NLP in a Multicultural World. 
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NAACL 2025

Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

Istabrak Abbes

1

Presentations

Small Encoders Can Rival Large Decoders in Detecting Groundedness