The article discusses the performance of various language models in understanding and generating meanings of words based on their training and context provided by corpora.
The article highlights a recent investigation by Lydia Risberg and her colleagues at the Estonian Language Institute into the performance of large language models in interpreting word meanings. Specifically, they compared the models' ability to generate adequate meanings based on a given corpus. The results revealed that the Claude Opus 4.1 model succeeded 85% of the time, GPT-4o 81%, and Gemini 2.5 Pro 73%. This indicates a significant distinction in the models' effectiveness when utilizing external context versus relying solely on pre-training information.
Interestingly, the study found that the performance of these models deteriorated when they depended only on the textual data seen during their pre-training. The team assessed the adequacy of meanings provided by the models under these constrained conditions, observing that only 45-55% were deemed appropriate. This further underscores the importance of corpus enrichment; by providing models with richer context, they minimize the risk of erroneous assumptions and enhance their semantic comprehension.
Ultimately, the findings suggest that while large language models like Claude, GPT, and Gemini are powerful tools for language processing, their effectiveness can be significantly influenced by the quality and contextual support of the data they use. The study serves as both a reinforcement of the importance of contextual data in AI training and a reminder of the limitations inherent in relying solely on a model's internal memory without external aid.
