GLGE: A New General Language Generation Evaluation Benchmark
In the field of NLP, evaluating the performance and generalization capabilities of LLMs remains a formidable challenge. The paper "GLGE: A New General Language Generation Evaluation Benchmark" introduces the General Language Generation Evaluation (GLGE) benchmark, specifically designed to assess Natural Language Generation (NLG) capabilities across a diverse set of tasks. While the benchmarks such as GLUE and SuperGLUE have traditionally focused on Natural Language Understanding (NLU), GLGE addresses the need for a comprehensive evaluation framework for NLG models.
Benchmark Design and Principles
GLGE distinguishes itself by embracing task diversity and difficulty within its framework. It comprises eight NLG tasks, ranging from text summarization to dialogue management. To cater to varied evaluation needs, GLGE includes three levels of difficulty—GLGE-Easy, GLGE-Medium, and GLGE-Hard. The construction of GLGE tasks is grounded in principles such as diversity in task formats, controlled task difficulty, ease of automatic evaluation, and utilization of popular datasets. By incorporating widely-accepted datasets and introducing new real-world datasets, GLGE ensures both relevance and rigor.
Task and Dataset Composition
The GLGE benchmark employs a selection of widely-used and novel datasets for its eight evaluation tasks, including:
- Text Summarization: GLGE features datasets such as CNN/DailyMail and Gigaword.
- Answer-aware Question Generation: Utilizing SQuAD 1.1 and a newly introduced dataset MSQG.
- Conversational Question Answering: Deployment of the CoQA dataset.
- Personalizing Dialogue: Assessment using PersonaChat.
These tasks are characterized by their distinct input-output configurations, offering a comprehensive platform to evaluate various facets of NLG models.
Evaluation Metrics and Overall Score
To facilitate objective comparison, GLGE employs standardized metrics like ROUGE, BLEU, METEOR, and F1-score across its tasks. The benchmark also defines an overall scoring mechanism that combines individual task scores, providing a single, aggregate performance measure.
Baseline Models and Results
The paper evaluates multiple baselines—non-pretrained models like vanilla LSTM Seq2Seq and Transformer, as well as several pretrained models such as MASS, BART, and ProphetNet. The results highlight a significant performance gap between pretrained and non-pretrained models. Larger pretrained models like BART\textsubscript{large} and ProphetNet\textsubscript{large} achieve competitive scores, underscoring the effectiveness of model pretraining.
Implications and Future Directions
The introduction of GLGE represents a meaningful step toward a unified evaluation standard for NLG tasks. Practically, it encourages improved model designs by providing diverse and challenging benchmarks. Theoretically, it facilitates understanding of model capabilities across varied NLG tasks. As researchers continue to evolve AI technologies, GLGE offers a pivotal resource for developing and assessing the next generation of NLG models.
Future work may focus on integrating advanced metrics such as BERTscore and BLEURT for richer evaluation, alongside exploring correlations with human judgment. The collaborative aspect of GLGE through a public leaderboard invites ongoing contributions from the research community, promoting transparency and progression in model evaluation standards.