Language Models as Evaluators : A Novel Framework for Automatic Evaluation of News Article Summaries

Sammanfattning: The advancements in abstractive summarization using Large Language Models (LLMs) have brought with it new challenges in evaluating the quality and faithfulness of generated summaries. This thesis explores a human-like automated method for evaluating news article summaries. By leveraging two LLMs with instruction-following capabilities (GPT-4 and Claude), the aim is to examine to what extent the quality of summaries can be measured by predictions of an LLM. The proposed framework involves defining specific attributes of desired summaries, which are used to design generation prompts and evaluation questions. These questions are presented to the LLMs in natural language during evaluation to assess of various summary qualities. To validate the effectiveness of the evaluation method, an adversarial approach is employed, in which a dataset comprising summaries with distortions related to various summary attributes is generated. In an experiment, the two LLMs evaluate the adversarial dataset, and their ability to detect known distortions is measured and analyzed. The findings suggest that the LLM-based evaluations demonstrate promise in detecting binary qualitative issues, such as incorrect facts. However, the reliability of the zero-shot evaluation varies depending on the evaluating LLM and the specific questions used. Further research is required to validate the accuracy and generalizability of the results, particularly in subjective dimensions where the results of this thesis are inconclusive. Nonetheless, this thesis provides insights that can serve as a foundation for future advancements in the field of automatic text evaluation.