Metadata-Version: 2.2
Name: expert-score
Version: 0.0.1
Summary: The implementation of the ExPerT score.
Home-page: https://github.com/alirezasalemi7/ExPerT
Author: Alireza Salemi
Author-email: Alireza Salemi <asalemi@cs.umass.edu>
License: MIT License
        
        Copyright (c) 2025 Alireza Salemi
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
        copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
        AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
        LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
        SOFTWARE.
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE.txt
Requires-Dist: backoff==2.2.1
Requires-Dist: json5==0.9.25
Requires-Dist: openai==1.60.1
Requires-Dist: parse==1.20.2
Requires-Dist: protobuf==5.29.3
Requires-Dist: setuptools==75.2.0
Requires-Dist: tqdm==4.66.5
Requires-Dist: transformers==4.45.2
Requires-Dist: vllm==0.6.6
Requires-Dist: google-generativeai==0.8.4
Dynamic: author
Dynamic: home-page

# ExPerT: Effective and Explainable Evaluation of Personalized Long-Form Text Generation

Evaluating personalized text generated by large language models (LLMs) is challenging, as only the LLM user, i.e. prompt author, can reliably assess the output, but re-engaging the same individuals across studies is infeasible. This paper addresses the challenge of evaluating personalized text generation by introducing ExPerT, an explainable reference-based evaluation framework. ExPerT leverages an LLM to extract atomic aspects and their evidences from the generated and reference texts, match the aspects, and evaluate their alignment based on content and writing style—two key attributes in personalized text generation. Additionally, ExPerT generates detailed, fine-grained explanations for every step of the evaluation process, enhancing transparency and interpretability. Our experiments demonstrate that ExPerT achieves a 7.2\% relative improvement in alignment with human judgments compared to the state-of-the-art text generation evaluation methods. Furthermore, human evaluators rated the usability of ExPerT's explanations at 4.7 out of 5, highlighting its effectiveness in making evaluation decisions more interpretable.
