Evaluation Metrics in the Era of GPT -4: Reliably Evaluating Large Language Models on Sequence to Sequence Tasks (2023)

Abstract: Large Language Models (LLMs) evaluation is a patchy and inconsistent landscape, and it is becoming clear that the quality of automatic evaluation metrics is not keeping up with the pace of development of generative models. We aim to improve the understanding of current models’ performance by providing a preliminary and hybrid evaluation on a range of open and closed-source generative LLMs on three NLP benchmarks: text summarisation, text simplification and grammatical error correction (GEC), using both automatic and human evaluation. We also explore the potential of the recently released GPT-4 to act as an evaluator. We find that ChatGPT consistently outperforms many other popular models according to human reviewers on the majority of metrics, while scoring much more poorly when using classic automatic evaluation metrics. We also find that human reviewers rate the gold reference as much worse than the best models’ outputs, indicating the poor quality of many popular benchmarks. Finally, we find that GPT-4 is capable of ranking models’ outputs in a way which aligns reasonably closely to human judgement despite task-specific variations, with a lower alignment in the GEC task.

## introduction in recent years, large language models (llms), particularly transformer based @xcite , have shown remarkable abilities across a wide range of nlp tasks. with the recent advances in capabilities of general-purpose generative models @xcite , a range of nlp tasks can be reformulated as generation tasks. robust evaluation is still an unsolved problem and established automatic evaluation metrics have been found to be poor surrogates, correlating weakly with human judgement @xcite . there is often no clear consensus on how these models should be evaluated @xcite . human evaluation has often been considered as the trusted evaluation method, though issues with human evaluation have also been widely acknowledged @xcite , e.g. it can be difficult to reproduce @xcite . nonetheless, a human evaluation study remains one of the best tools to sensibly assess any bias or limitation with automatic metrics @xcite . recent evaluation work has often focused on a single task @xcite , a single model @xcite , a single dataset @xcite or automatic evaluation @xcite . in this work, we carry out a multidataset, multi-model, multi-task hybrid evaluation using automatic metrics, human evaluation, and model-to-model evaluation with @xcite . 1 we explore the open and closed-source llms space to sample the current landscape of available models and evaluate them on the following sequence-to-sequence tasks, reframed as text generation tasks without the requirement for task-specific fine-tuning: text summarisation, text simplification, and grammatical error correction (gec). these are our main findings: firstly, we show how traditional reference-based evaluation metrics are inadequate at predicting or replacing human judgement. it is unclear whether this is due to the limitations of the metrics or to the poor quality of references of large open source datasets, or both. while automatic metrics might have been an adequate proxy to evaluate previous models, they seem unable to reliably capture the performance of latest-generation llms which now generate ac-ceptable output that is significantly different from the gold reference. secondly, we prove that even open-source models outperform the gold standard reference of large and well-established datasets according to human evaluators. this shows how data quality is now one of the main bottlenecks in evaluation research. finally, we reveal how gpt-4 has reasonable alignment with human judgement when ranking different models on most tasks and metrics; we did however observe some variations, with lower alignment in some metrics than in others. our code is available at https://github.com/ protagolabs/seq2seq_llm_evaluation . ## evaluation metrics we analysed models' outputs using both automatic metrics and human evaluation, and assessed the ability of the recently released gpt-4 model to act as a reviewer. ## conclusion model evaluation is a topic which is attracting increasing interest from the community. @xcite have recently published an extensive evaluation report on llms, however they mostly focused on automatic evaluation. prompted by the recent advances in generative capabilities of the latest llms, we conducted this study to explore the drift between human judgement and automatic, reference-based evaluation of zero-shot model performance. we also explored model-to-model evaluation with gpt-4. the study was conducted using large, open-source datasets often acting as benchmarks for their respective tasks. our work reveals a systematic misalignment between reference-based automatic metrics and human evaluation on a range of generative tasks, highlighting the inadequacy of the gold reference in the public nlp benchmarks. it is not clear whether this misalignment is purely due to the limitations of automatic metrics, or whether poor reference quality makes using any reference-based comparative metrics unreliable. despite chatgpt being rated one of the best models on most metrics by human reviewers, the best open-source llms also consistently outperformed the reference outputs. we also explored the potential of gpt-4 to act as a reviewer and found it has strong correlation with human judgement for summarisation and simplification tasks, and moderate correlation for gec. future work will look at improving the quality of prompts, providing few-shot in-context learning @xcite , or exploring the potential of chain-of-thought prompting @xcite in improving models' outputs. given the misalignment mentioned above, extending human evaluation to larger datasets and to a wider range of model settings will also be of particular future interest, so as to minimise the bias introduced when using automatic metrics to select a subset for human evaluation. finally, introducing multiple automatic evaluation metrics (e.g. reference-less) for each task might help deepen our understanding of the relation between such metrics and human judgement.