Are Large Language Model-based Evaluators the Solution to Scaling Up Multilingual Evaluation? (2024)

Abstract: Large Language Models (LLMs) excel in various Natural Language Processing (NLP) tasks, yet their evaluation, particularly in languages beyond the top 20, remains inadequate due to existing benchmarks and metrics limitations. Employing LLMs as evaluators to rank or score other models’ outputs emerges as a viable solution, addressing the constraints tied to human annotators and established benchmarks. In this study, we explore the potential of LLM-based evaluators in enhancing multilingual evaluation by calibrating them against 20K human judgments across three text-generation tasks, five metrics, and eight languages. Our analysis reveals a bias in LLM-based evaluators towards higher scores, underscoring the necessity of calibration with native speaker judgments, especially in low-resource and non-Latin script languages, to ensure accurate evaluation of LLM performance across diverse languages.

## introduction large language models (llms) can achieve remarkable results on a variety of tasks, sometimes even outperforming humans on certain tasks and domains @xcite @xcite . however, measuring the performance of llms is challenging, as standard nlp benchmarks may not reflect real-world applications. other hurdles for llm evaluation include the scarcity of benchmarks for diverse and complex tasks, benchmark saturation, contamination of benchmark data in llm training data, and the weak correlation between automated metrics and human judgment @xcite @xcite . therefore, researchers have proposed alternative evaluation methods that go beyond benchmarking to assess the abilities and limitations of llms @xcite . while llms excel at various tasks in english, their capabilities in other languages are more limited. this disparity may increase the digital divide, preventing a significant portion of the global population from benefiting from llms and potentially harming them. @xcite conduct a comprehensive benchmarking of llms across the available multilingual benchmarks covering several tasks and languages, and show that the performance of llms degrades significantly on languages that are transcribed in non-latin scripts and under-resourced languages. multilingual evaluation is challenging to scale. certain language families, such as indo-european, are over-represented in multilingual benchmarks with other language families having very little presence. there is a scarcity of multilingual benchmarks designed to assess tasks that simulate actual llm usage in real-world scenarios. the metrics used in these benchmarks may be unsuitable for languages with rich morphology or complex writ-ing systems, as well as phenomena arising from language contact such as borrowing, code-mixing, and transliteration. evaluation by native speakers is the gold standard for building an accurate picture of model performance, especially in complex tasks without well-defined automated metrics. however, budget constraints, turnaround time, and the lack of easy access to native speakers in some languages all pose challenges in scaling evaluation. this leads to a situation in which llm performance is unknown for most languages of the world @xcite . the success of llms in complex tasks such as sentiment analysis, reasoning, problem-solving @xcite , and providing feedback for reducing llm harms @xcite has led to the question of whether llms can replace human annotators, or help augment human evaluation @xcite . utilizing llms as multilingual evaluators is, therefore, an attractive option to decrease costs and circumvent the challenges of scaling assessments by native speakers. however, llms have been demonstrated to have inferior performance even in some high-resource languages and have not been evaluated extensively across many languages on dimensions such as toxicity, fairness, and robustness (due to the absence of such benchmarks) @xcite , it is prudent to proceed with caution. failing to do so can lead to misleading results which may further widen the digital divide. in this work, we study whether llm-based evaluation can be the answer to scaling up multilingual evaluation. in other words, can llms serve as substitutes or supplements for human native speakers in delivering useful and accurate insights regarding llm outputs in non-english languages, while considering diverse aspects of interest like linguistic acceptability, task accomplishment, and safety? our main contributions are as follows: ## related work broadly, there are two main uses of llms as evaluators: llms can be used as alternatives to metrics that compare human and machine-generated text, such as bleu @xcite and rouge @xcite . word overlap-based metrics are limited, and llm-based scorers have been shown to outperform them. gptscore @xcite is a popular llm-based framework that can be used to score model outputs based on human-created references along various dimensions. however, these scores still rely on having examples of humancreated reference data. the second use case of llms as evaluators is when the llm is presented with the output of a system (usually an llm, sometimes the same model) and asked to judge its quality or safety without any human output to compare against @xcite . the llm is instructed on how to perform this evaluation with the help of the task description, evaluation rubric, and sometimes, one or more examples in the prompt. this is the use case we focus on in this work. @xcite prompt chatgpt to annotate tweets across various dimensions such as topic and stance and find that it outperforms crowdworkers. @xcite explore the use of gpt3.5 as an evaluator for abstractive summarization and find that although gpt is a useful evaluator, as the quality of summarization improves, the quality of evaluation degrades. along similar lines, @xcite evaluate chatgpt on various nlg tasks and find that it has a high correlation with human judgments. @xcite evaluate the effectiveness of llms on evaluation of translation quality and find that llms starting from gpt3.5 and above achieve sota performance on translation evaluation benchmarks. @xcite leverage llms for finegrained annotation of errors in machine translation outputs. llm-based evaluators have also been used to score and refine outputs they produce, as described in @xcite , ultimately producing outputs that are scored higher on human and automated metrics than the original outputs. @xcite explore the use of llmbased evaluators on scoring written discourse for coherence and find a strong correlation with human judgments. the success of llm-based evaluators has led many to question whether llm-based evaluation can replace or augment human evaluation @xcite . however, there have been studies showing that llm-based evaluators can have some biases. @xcite demonstrate that llms tend to prefer answers with factual errors when they are too short or contain grammatical errors. @xcite highlight the need for validating llm-based evaluators on a task-by-task basis. @xcite perform nlg evaluation using gpt-4 and find that although it correlates well with human judgments, it may potentially be biased towards preferring llm-generated texts. @xcite show that llms have egocentric bias where they prefer to rank their own outputs highly in evaluation. @xcite point out that gpt4-based evaluators have positional bias and scores can be easily altered by changing the order of appearance. there are also several ethical issues with the use of llms as evaluators described in @xcite . @xcite suggest that wider and deeper llms are fairer evaluators, while @xcite introduce a framework for multiple evaluator agents to reach a consensus, mimicking the situation of having multiple annotators. although there has been some work measuring the calibration of llm-based evaluators to human judgments @xcite , previous studies have focused on english, and ours is the first work (to the best of our knowledge) that addresses this problem in the multilingual context. ## experimental setup we perform experiments on a text generation application that is powered by gpt-4, and evaluate the following sub-tasks: open prompt: this task processes a concise prompt to generate a document adhering to the provided guidelines, producing up to 2, 048 tokens, approximately equivalent to one page in english or spanish, and marginally less in other languages. continue writing: this task takes two textual inputs, termed "left" and "right" to generate a coherent continuation between them, accommodating up to 1, 000 tokens. notably, one of the inputs may be omitted. summarize: engages in standard summarization by condensing a document of at least 500 words into a succinct summary. it allows for an optional user-defined prompt to tailor the summary format, such as highlighting key points. we cover the following languages: english (en), french (fr), german (de), spanish (es), chinese (zh), japanese (ja), italian (it), brazilian portuguese (pt-br), and czech (cs). of these, the first six are classified as very high resource languages (class 5, or "the winners"), while the last three are classified as class 4 ("the underdogs") according to @xcite . we plan to extend our study to lower-resource languages in the future. we study the following dimensions of interest: linguistic acceptability (la): this measures whether the text sounds right to a native speaker. the values of this metric are {0, 1, 2}, with 0 corresponding to not acceptable, 1 corresponding to some errors, but acceptable and 2 to perfectly acceptable. we chose la as opposed to grammaticality to ensure a comparable, native-speaker-led evaluation that did not require formal training in the language. output content quality (ocq): whether the general quality of the content is good or not, with values {0, 1, 2}. a score of 0 could indicate that the output is in the wrong language, is repetitive, or sounds like it has been scraped from the web, or translated. a score of 1 indicates that the output is okay in terms of grammar and word choice but still sounds awkward in the language. a score of 2 indicates that the text is of high quality. task quality (tq): this measures the ability of the model to follow the given instructions in the prompt. the values of this metric are {0, 1, 2}, with 0 indicating that the model did not follow the instructions at all. likewise, a score of 1 indicates that the model followed the instructions approximately well and 2 that it followed perfectly well. the difference between tq and ocq is that the latter focuses on whether the content is appealing to a user, while tq emphasizes the ability of the model to follow the given instructions. problematic content (pc): whether there was any offensive or problematic content in the output. this is a binary metric, with 0 indicating that the output contains this type of content. hallucinations (h): this measures how wellgrounded the model's output was to the input con-tent, and/or whether the model output counterfactual information conflicted with the input content. it is a binary metric, with 0 indicating the presence of hallucinations. ## discussion overall, our results indicate that gpt-based evaluators have relatively high consistency for non-english languages when set to a temperature of 0. en fr de es it pt-br cs ja zh languages 0.0 0.2 0.4 0.6 0.8 1.0 percentage of sensitive samples sensitivity analysis open prompt continue writing summarize ## ethical considerations we use the framework by @xcite to discuss the ethical considerations for our work.