Brief

Researchers have developed an "attention expansion" mechanism to improve keyphrase extraction from long documents. This method augments pre-trained language model representations with information from surrounding text chunks, effectively broadening the model's contextual scope without needing full-document attention or costly large language model inference. Experiments across various models and datasets show consistent performance gains, establishing attention expansion as an efficient strategy for long-document keyphrase extraction. AI

Researchers have developed a new framework to interpret how automated scoring models assign quality ratings to complex language performances, such as classroom transcripts. This framework combines model-agnostic Shapley-value attributions with explanations generated by large language models (LLMs). In tests on the CLASS framework's Quality of Feedback dimension, Shapley values proved more reliable and transferable than LLM-generated rationales for explaining model predictions. AI