Beyond Base Predictors: Using LLMs to Resolve Ambiguities in Akkadian Lemmatization

AbstractWe present a hybrid approach for Akkadian lemmatization in the EvaCun 2025 Shared Task that combines traditional NLP techniques with large language models (LLMs). Our system employs three Base Predictors–a dictionary lookup and two T5 models–to establish initial lemma candidates. For cases where these pre-dictors disagree (18.72% of instances), we im-plement an LLM Resolution module, enhanced with direct access to the electronic Babylonian Library (eBL) dictionary entries. This module includes a Predictor component that generates initial lemma predictions based on dictionary information, and a Validator component that refines these predictions through contextual rea-soning. Error analysis reveals that the system struggles most with small differences (like cap-italization) and certain ambiguous logograms (like BI). Our work demonstrates the benefits of combining traditional NLP approaches with the reasoning capabilities of LLMs when provided with appropriate domain knowledge.