Lost in the Source Language: How Large Language Models Evaluate the Quality of Machine Translation (2024)

Abstract: This study investigates how Large Language Models (LLMs) leverage source and reference data in machine translation evaluation task, aiming to better understand the mechanisms behind their remarkable performance in this task. We design the controlled experiments across various input modes and model types, and employ both coarse-grained and fine-grained prompts to discern the utility of source versus reference information. We find that reference information significantly enhances the evaluation accuracy, while surprisingly, source information sometimes is counterproductive, indicating LLMs’ inability to fully leverage the cross-lingual capability when evaluating translations. Further analysis of the fine-grained evaluation and finetuning experiments show similar results. These findings also suggest a potential research direction for LLMs that fully exploits the crosslingual capability of LLMs to achieve better performance in machine translation evaluation tasks.

heading: 1 Introduction
text: The last decade has witnessed significant development in Neural Machine Translation (NMT) (Bahdanau et al., 2015; Vaswani et al., 2017; Hassan et al., 2018). As the quality of machine translations has been improved, it becomes more challenging and critical for automatic translation evaluation. The recent study (Freitag et al., 2022) calls for stopping using BLEU (Papineni et al., 2002), a traditional metric, as it is not reliable for highquality translations and has a lower correlation with human judgements. Neural metrics based on pre-trained language models (Devlin et al., 2019; Liu et al., 2019; Conneau et al., 2020), such as COMET (Rei et al., 2020), show a higher correlation with human judgments. However, these neural
∗Work was done during internship at Tencent AI Lab. †Corresponding authors
metrics only provide a score lacking interpretability and still exhibit robustness issues hard to detect (Yan et al., 2023). Recently, Large Language Models (LLMs) (OpenAI, 2023; Touvron et al., 2023; Wang et al., 2023b) have also been used as translation evaluators. GEMBA (Kocmi and Federmann, 2023b) presents that GPT-4 can achieve state-of-the-art performance in system-level assessment. While LLMs show remarkable performance in translation evaluation tasks, the reasons underlying their success have not been thoroughly investigated.
In this paper, we take the further step to investigate how LLMs leverage source and reference information in evaluating translation in both coarsegrained and fine-grained settings, bringing better understanding of the working mechanism of LLMs. Four input modes are defined, each of which exposes different information to LLMs. These include Translation-only (T), Source-Translation (S-T), Reference-Translation (R-T) and SourceReference-Translation (S-R-T) modes. We first instruct both open and closed LLMs to predict coarsegrained quality scores using GEMBA prompt, but given different information, namely sources and references. While references significantly improve the system-level accuracy and segment-level correlations, we surprisingly find that the source information is sometimes counterproductive. For
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example, as shown in Table 1, ChatGPT1 achieves the best performance in the R-T mode, while S-RT mode leads to performance degradation. This indicates LLMs’ inability to fully leverage crosslingual capabilities when evaluating translation sentences, despite ChatGPT’s impressive performance in multi-lingual or translation tasks.
In addition to the superficial score prediction, we further explore the fine-grained error detection to better understand the cross-lingual ability. Our fine-grained experiments confirm the above observations, where we follow AutoMQM (Fernandes et al., 2023) method to predict error spans and categories for translation sentences and study the performance of LLMs with different input modes. We also conduct a comprehensive meta-evaluation for the error span and error category, alongside executing a critical error detection task. The findings suggest that LLMs struggle to fully utilize the source information for translation evaluation.
Lastly, we examine the effect of fine-tuning, which makes deeper modifications to the model, with Multidimensional Quality Metrics (MQM) data (Freitag et al., 2021a). Although fine-tuning can greatly improve the model’s performance of translation evaluation, the negative impact of source sentence still exists. These experimental results reveal the limitation of current LLMs on machine translation evaluation tasks and suggest a potential research direction that fully exploits the cross-lingual capability of LLMs to achieve better performance.
Overall, our main contributions are as follows:
• To the best of our knowledge, we are the first to explore the working mechanism of LLMs in evaluating translation by testing the importance of sources and reference information.
• We conduct extensive experiments to discern the utility of source versus reference information through various aspects, suggesting that LLMs are unable to fully utilize the crosslingual capability to evaluate translation sentences.
• We provide an in-depth analysis of translation error detection. Our code and data would be released for the research community to promote the development of LLMs in automatic translation estimation tasks.2
1We use gpt-3.5-turbo-0613 in our experiments. 2Code and data are available at https://github.com/
NJUNLP/lost_in_the_src.

heading: 2 Related Work
text: Automatic Translation Evaluation. Automatic evaluation has been a crucial and tough problem along with the development of machine translation. Traditional metrics like BLEU (Papineni et al., 2002), METEOR (Banerjee and Lavie, 2005) and chrF (Popovic, 2015) rely heavily on n-gram matching algorithms. Despite their past success, they fail to keep pace with the increasing performance of translation models that generate semantically correct translations.
Neural metrics leverage the meaningful representations of pre-trained language models to evaluate translations. BertScore (Zhang et al., 2020) and MoverScore (Zhao et al., 2019) calculate similarity using these representations, while generationbased methods like Prism (Thompson and Post, 2020) and BartScore (Yuan et al., 2021) assess text quality through generation tasks, conditioned on sources or references. Learned metrics such as COMET (Rei et al., 2020), BLEURT (Sellam et al., 2020), UniTE (Wan et al., 2022) and XCOMET (Guerreiro et al., 2023) apply neural networks to predict quality scores in a supervised manner. Particularly, UniTE suggests that the interaction between source and hypothesis may have an adverse effect. Although UniTE performs best with the S-R-T mode, our work shows this is not always the case for LLMs.
LLMs that can follow the instructions of evaluation tasks are also used to evaluate translations. GEMBA (Kocmi and Federmann, 2023b) shows that GPT-4, when asked to directly generate a quality score, is the state-of-the-art translation evaluator at that time in the system-level assessment. EAPrompt (Lu et al., 2023), AutoMQM (Fernandes et al., 2023), and GEMBA-MQM (Kocmi and Federmann, 2023a) endeavor to instruct LLMs in detecting translation errors meticulously and labelling the error category and severity. Despite their remarkable performance, the reasons underlying their success have not been thoroughly investigated.
LLM-based Text Evaluation. Evaluating text quality is a challenging problem even for humans. LLMs with broad world knowledge and expertise in linguistics, like ChatGPT, have been used as natural language evaluators. GPTScore (Fu et al., 2023) utilizes the conditional probability for text evaluation. Other works (Wang et al., 2023a; Liu et al., 2023a; Wang et al., 2023c; Chan et al., 2023;
Score the following translation from {src_lang} to {tgt_lang} with respect to the human reference on a continuous scale from 0 to 100 that starts on "No meaning preserved", goes through "Some meaning preserved", then "Most meaning preserved and few grammar mistakes", up to "Perfect meaning and grammar".
Liu et al., 2023b) prompt ChatGPT to generate evaluations for natural texts using various techniques. InstructScore (Xu et al., 2023) proposes an explainable metric by fine-tuning the Llama-7B with data generated from GPT-4 and self-generated outputs, proving the feasibility of using open, smaller models for evaluation purposes. This study primarily focuses on employing LLMs for assessing translations, with a major emphasis on the cross-lingual ability. However, the insights gained from our research may extend to other NLG evaluation tasks. LLMs demonstrate a greater ability to capitalize on reference information, while they may face challenges in effectively utilizing task input like source information.

heading: 3 Coarse-grained Score Prediction
text: We first investigate how LLMs leverage the source and reference information in conducting coarsegrained evaluations of translation quality via score prediction. We adopt the GEMBA-SQM prompt template (Kocmi and Federmann, 2023b), as shown in Figure 1. The inclusion of source and reference information in the prompt varies based on the selected input mode. For instance, in the R-T mode, the source text in red is omitted from the prompt. We assess the efficacy of LLMs across four distinct input modes, examining their impact on the model’s performance.

heading: 3.1 Experimental Setup
text: Data. We use the test set from WMT22 Metric Shared Task (Freitag et al., 2022) which contains the MQM annotated data for three translation directions: En-De, Zh-En, and En-Ru. Reference A (refA) is used as the standard reference. The quality of references can affect the performance of reference-based metrics (see Appendix C). The golden quality score is calculated
from the MQM ratings annotated by humans. The weighting scheme of each error severity and category can be found in Freitag et al. (2021a).
Models. We evaluate both the closed model GPT3.5-turbo and open models, including the Llama2Chat series (Touvron et al., 2023) and Mistral-7BInstruct (Jiang et al., 2023). We only consider the chat version of these models because base models without alignment may not follow instructions according to our preliminary study. All of these models possess a certain level of translation ability in the specified language pairs (Zhu et al., 2023).
Evaluation Metrics. Following Kocmi and Federmann (2023b), we use the system-level accuracy and segment-level Kendall’s τ correlation as our primary evaluation metrics, complemented by the Pearson correlation ρ. We use the PERM-BOTH hypothesis test with 1000 resampling runs and p=0.05 (Deutsch et al., 2021).

heading: 3.2 Results
text: Table 2 demonstrates the main results of the metaevaluation of the coarse-grained translation quality score, in which we include COMET-22 (Rei et al., 2022), BLEU and chrF as baselines.
One of the most surprising findings is that the RT mode is the most effective among the four modes in most cases. The R-T mode surpasses other modes in system-level accuracy and segment-level correlations across models, particularly excelling with strong models like GPT-3.5 and Llama2-70BChat. This suggests that the reference information can significantly enhance the evaluation accuracy, but the source information has little or no impact on the translation evaluation task. On the other hand, the numerous lower scores in S-T mode compared to T mode also indicate that LLMs do not fully utilize their cross-lingual capability and may even be confused by the source in this task. We further
calculate the Shapley values (Shapley, 1953) that assess the contributions of the source and reference part, as shown in Table 3. Higher number means more positive influence, and vice versa. A positive number means that the information has a positive effect while a negative number means a negative effect. The way to calculate Shapley Values can be found in Appendix D. The reference parts contribute more than the source parts, which can even have negative impacts.
There is another unexpected observation that the T mode can achieve much better performance than a random guess. We posit that LLMs evaluate translations solely based on fluency, which is positively correlated to the translation quality. Our further analysis by log-probability in Appendix E supports this hypothesis.
Compared to baseline metrics, our findings align with those of Kocmi and Federmann (2023b). LLMs are better at system-level evaluation but are inferior at segment-level correlations than COMET22. Metrics based on strong LLMs outperform both BLEU and chrF. COMET-22 is built upon the pre-trained encoder language model XLM-R (Conneau et al., 2020) and it is an ensemble between several models with different input. It’s sequence tagger performs better with the S-R-T mode at the segment level. We suspect that the underlying mechanism of encoder-only models may differ from decoder-only models. Moreover, COMET is fine-tuned with a mount of task-specific data including both the source and the reference. This may also enhance the performance of COMET on this task.
Based on the given source and reference, identify the major and minor errors in this translation. Note that Major errors refer to actual translation or grammatical errors, and Minor errors refer to smaller imperfections, and purely subjective opinions about the translation.

heading: 4 Fine-grained Error Detection
text: While coarse-grained scoring methods have demonstrated their potential, recent innovations like AutoMQM and GEMBA-MQM are eliciting the capabilities of LLMs through the use of specialized prompts, leading to more refined and interpretable results. We further dive into studying how well LLMs leverage the different information with finegrained methods.
We adopt the AutoMQM prompt template (Fernandes et al., 2023), as illustrated in Figure 2. The content in the yellow part varies depending on the input mode. Our assessment encompasses three perspectives: MQM scores, error spans, and error categories, hence offering a comprehensive diagnosis of the model’s predictions.

heading: 4.1 Experimental Setup
text: Data. We sample a portion of the WMT22 test set as our test set due to limited budgets (see Appendix F). Specifically, we uniformly sample 200 source sentences and all corresponding system outputs from the test set. There are 16 systems with MQM scores in the En-De and Zh-En directions, resulting in a total of 3200 samples for each direction. Following Fernandes et al. (2023), the in-context demonstrations are sampled from the data in WMT21 Metric Shared Task (Freitag et al., 2021b). The number of in-context demonstrations is 4 and stratified sampling with a set of rejection criteria is used.3 Since there are no MQM ratings for the En-Ru direction in the WMT21 dataset, we
3These models have terrible performance using this prompt without demonstrations, as shown in Fernandes et al. (2023)
only assess the other two directions.
Models. We evaluate the GPT-3.5-turbo and the Llama2 base series. In our preliminary study, the Llama2 chat models cannot follow the output format in this prompt. Therefore, we decide to assess the base models only. All models in this experiment generate text using greedy decoding.
Meta Evaluation. Based on the identified error categories and severity, we compute an MQM score for each sample according to Google’s MQM error weighting (Freitag et al., 2021a). Since we do not predict sub-categories, we only assign a score of −5 for a major error and −1 for a minor error. We adopt the previous metrics to evaluate the MQM scores.
We also assess the quality of the identified error spans. Similar to Fernandes et al. (2023), we calculate the precision, recall, F1 score, and Matthews Correlation Coefficient (MCC) for the predicted error spans. In particular, given the gold error spans S = {e1, . . . , en}, ej = {wi, wi+1, . . . } denotes each error span containing the wrong words, where wi is the i-th word in the sentence. The span of each error is P (ej) = {i|wi ∈ ej}. Then we count the span overlap based on the set P (S) = ⋃n j=1 P (ej). The span precision (SP) and span recall (SR) of the predicted error spans Ŝ are defined as follows:
SP = |P (S) ∩ P (Ŝ)|
|P (Ŝ)| (1)
SR = |P (S) ∩ P (Ŝ)|
|P (S)| (2)

heading: AutoMQM

heading: GEMBA
text: The span F1 score (SF1) is the harmonic mean of SP and SR. Since major errors contribute most to the quality score, we calculate the major precision (MP) and major recall (MR) as follows:
MP = |P (Smaj) ∩ P (Ŝmaj)|
|P (Ŝmaj)| (3)
MR = |P (Smaj) ∩ P (Ŝmaj)|
|P (Smaj)| (4)
where Smaj ⊆ S is the subset only containing major errors, and major F1 (MF1) score is the harmonic mean. Note that our MR is slightly different from Fernandes et al.’s (2023) MR, which takes
into account both minor and major prediction errors. In this way, we can better evaluate the performance of predicting the major errors.
In addition, we calculate the precision, recall, and F1 score for the error category. Specifically, let Cat(e) denote the error category, and Cat(S) = (Cat(e1), . . . ,Cat(en)) denote the gold labels. The function Count(S, c) calculates the count of occurrences of category c within Cat(S). The precision and recall of the category c are defined as:
Pc = min(Count(S, c),Count(Ŝ, c))
Count(Ŝ, c) (5)
Rc = min(Count(S, c),Count(Ŝ, c))
Count(S, c) (6)
And the F1c score is the harmonic mean of the precision and recall. Here we ignore the sub-categories since AutoMQM does not predict sub-categories. Note that these three scores only consider the error categories and do not necessitate the correct identification of error positions for simplicity.

heading: 4.2 Results
text: Score Meta-evaluation. Table 4 shows that GPT3.5, Llama2-70B, and Mistral-7B achieve the best or second-best score with the R-T mode, suggesting a limitation in their ability to employ cross-lingual capabilities for this task. However, while the T mode appears to yield the strong results for weak models such as Llama2-13B and Llama2-7B, it is important to note that their overall performance remains substantially low. This leads to the hypothesis that these weak models may not fully understand the task, thereby failing to effectively identify errors. Nevertheless, the contribution of the reference is much larger than that of the source in most cases, as shown in Table 6. These results also indicate the limitation of cross-lingual capabilities of LLMs to evaluate translations.
Besides, we also find something different with the conclusions of Fernandes et al. (2023). The AutoMQM prompt outperforms the GEMBA-SQM prompt when using GPT-3.5-Turbo, which is consistent with the previous work using PaLM2 (Anil et al., 2023), but this trend does not extend to the Llama2 series (the 7B and 13B models have a similar phenomenon). Due to the lack of training details on PaLM2, we speculate that PaLM2 may have a larger model scale and enhanced multilingual capabilities than the Llama2 series.
Span Meta-evaluation. The results of the span meta-evaluation presented in Table 5 demonstrate a similar pattern to the score meta-evaluation. GPT3.5-Turbo and Llama2-70B have better performance when using the R-T mode, while small models are not stable. Overall, the performance of the R-T mode still surpasses that of both the S-T and S-R-T modes. This suggests that the limitations in the cross-lingual capabilities of LLMs also exist in word-level translation evaluation tasks.
Unexpectedly, it appears that identifying major errors poses a greater challenge. The MF1 scores are consistently lower than the corresponding SF1 scores across all tested models. All of these models exhibit an apparently low level of performance, suggesting substantial room for progress in the error span prediction.
Category Meta-evaluation. Table 7 presents the outcomes of the category meta-evaluation. When scrutinizing the F1 scores across the models for each designated category, the overall performance is very poor despite the simplification. Notably, the scores of the Accuracy category surpass those of other categories, with GPT-3.5 demonstrating a relative advantage over the Llama2 models, particularly when provided with a reference.
The AutoMQM prompt lacks explicit definitions for each category, requiring the models to infer the meaning of each from the demonstrations. The Accuracy category predominates all other categories except the No-Error category. This prevalence likely biases the model towards a more frequent prediction of Accuracy errors. The remaining categories exhibit diminished F1 scores, which are attributed to the models’ limited understanding of the inherent semantics associated with each category due to their low frequency. This pattern persists irrespective of different models or input modes.
“No-Error” is a special category, as it is mutually exclusive with other error categories. For analytical simplicity, it is treated analogously to a category, with F1 scores computed accordingly. In this regard, GPT-3.5 exhibits a pronounced competence in identifying error-free samples in stark contrast to the Llama2 models. Weak models exhibit a propensity for overestimating the presence of errors.

heading: 4.3 Critical Error Detection
text: To better understand the cross-lingual ability of LLMs, we investigate whether they can detect the critical translation errors that are easy to discover. We extract 50 samples from the test set of WMT22’s Critical Error Detection Task (Zerva et al., 2022). Specifically, we only use the “BAD” samples from the En-De subset and manually label one critical error span for each sample. The samples with omission errors are excluded, keeping the addition errors, named entity errors, negation errors and number errors. We use the AutoMQM prompt with the S-T mode to determine whether LLMs can utilize the source information to identify the critical error spans. SP, SR, SF1 and accuracy are used to measure the performance. The accuracy here is calculated as the ratio of how many critical error spans are completely identified.
The results are demonstrated in Table 8. Strong models like GPT-3.5 and Llama2-70B can identify most errors. However, the precision is very low,
indicating that they tend to over-predict errors. On the other hand, there remains a noticeable probability, exceeding 25%, that they may overlook crucial information in the source. This suggests that LLMs cannot fully utilize the source information, leading to the failure of error detection. There some cases shown in Figure 3.

heading: 5 Fine-tuning LLMs with MQM data
text: We further investigate the effect of fine-tuning an open LLM with task-specific data to determine if it can eliminate the above limitation.

heading: 5.1 Experimental Setup
text: In this experiment, we integrate the En-De and ZhEn samples from the WMT21 dataset to form the supervised training set, and employ the WMT22 dataset as the test set. The organization of the training samples adheres to the Alpaca (Taori et al., 2023) instruction template, where the instruction
and the input parts are identical with the AutoMQM prompt. Regarding the output part, our format mirrors that of InstructScore (Xu et al., 2023), with the exception of the explanation component.4 Note that we also ignore the error sub-category here. To accommodate the three input modes, i.e. the S-T, R-T and S-R-T mode, each training sample is randomly assigned with one mode. The statistics of the training set in can be found in Table 9. We fine-tune the Llama2-7B base model for 3 epochs, using a decayed learning rate of 2e-5 and a batch size of 128.

heading: 5.2 Results
text: We have some interesting findings in Table 10. Firstly, the performance of the R-T mode remains significantly superior to that of the other two modes, indicating that the model still cannot make full use of the source information after naive fine-tuning. Secondly, The overall performance of the finetuned Llama2 is stronger than GEMBA-Llama27B-Chat, proving the effectiveness of further finetuning for this task. However, the distribution of the training data is crucial. The fine-tuned model outperforms GPT-3.5-turbo on Zh-En segment-level correlations with the proper data distribution. On the contrary, the performance on En-De direction degrades due to the extremely imbalanced En-De training data, where samples with No-error dominates, as shown in Table 9. To mitigate the problem of unbalanced distribution, we down-sample the No-error samples in the En-De corpus and keep about 30% No-error samples. The Zh-En samples remain unchanged. Fine-tuning with more balanced data can effectively enhance the En-De segment-level correlations, as shown in Table 10.
4In our preliminary experiments, we used the output format of AutoMQM, but the results were terrible.
The change of En-De data also brings benefits to other directions. Lastly, it is noteworthy that the evaluation capability, to some extent, can be transferred to the language pair not encountered in the fine-tuning stage. The fine-tuned model achieves even higher correlations without seeing any En-Ru samples, compared to GEMBA-Llama2-7B-Chat.

heading: 6 Conclusion
text: We empirically analyze how well LLMs incorporate the source and reference information for translation evaluation, comparing the effectiveness of open and closed LLMs through prompting and finetuning. Our results reveal their limitations in fully exploiting the cross-lingual capability for the task, with the inclusion of source information even occasionally proving detrimental to performance. Furthermore, our work contributes a detailed metaevaluation of spans and categories with the finegrained evaluation method, along with the critical error detection task. These findings not only furnish insights into the current capabilities and limitations of LLMs in translation evaluation, but also establish a foundational basis for subsequent scholarly endeavors. In the future, we would like to extend these analyses to other NLG evaluation tasks.

heading: 7 Limitations
text: We discuss the limitations and future research directions of our work in this section.
• In experiments, we mainly use the prompts from the previous works (Fernandes et al., 2023; Kocmi and Federmann, 2023b). These prompts are may not the best prompt that can fully elicit the ability of LLMs on this task. It’s important to note that our conclusion may
not apply to all prompts. However, the current popular prompts that simply ask LLMs to predict scores or fine-grained errors can be negatively affected by the source. Designing prompts that can better elicit the cross-lingual capability of LLMs is a topic for future research.
• We do not evaluate other closed LLMs like GPT-4 due to the limited resources. The tokens consumed in our experiments are recorded in the Appendix F. We leave assessing additional LLMs with more test data as future work.
• We do not dive into how to better fine-tune the open model. More carefully designed training data or pipelines may bring greater improvement for this task.
• In this work, we only focus on the translation evaluation task which is a sub-field of NLG evaluation tasks. Future research should focus on extending these analyses to other NLG evaluation tasks.

heading: Acknowledgements
text: We would like to thank the anonymous reviewers for their insightful comments. Shujian Huang and Zhirui Zhang are the corresponding authors. This work is supported by National Science Foundation of China (No. 62376116, 62176120), the Liaoning Provincial Research Foundation for Basic Research (No. 2022-KF-26-02), research project of Nanjing University-China Mobile Joint Institute.

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heading: A GEMBA-SQM Prompts with Four Input Modes

heading: T mode
text: Score the following translation from {src_lang} to {tgt_lang} on a continuous scale from 0 to 100 that starts on “No meaning preserved”, goes through “Some meaning preserved”, then “Most meaning preserved and few grammar mistakes”, up to “Perfect meaning and grammar”.
{tgt_lang} translation: “{translation}” Score (0-100):

heading: S-T mode
text: Score the following translation from {src_lang} to {tgt_lang} on a continuous scale from 0 to 100 that starts on “No meaning preserved”, goes through “Some meaning preserved”, then “Most meaning preserved and few grammar mistakes”, up to “Perfect meaning and grammar”.
{src_lang} source: “{source}” {tgt_lang} translation: “{translation}” Score (0-100):

heading: R-T mode
text: Score the following translation from {src_lang} to {tgt_lang} with respect to the human reference on a continuous scale from 0 to 100 that starts on “No meaning preserved”, goes through “Some
meaning preserved”, then “Most meaning preserved and few grammar mistakes”, up to “Perfect meaning and grammar”.
{tgt_lang} human reference: “{reference}” {tgt_lang} translation: “{translation}” Score (0-100):

heading: S-R-T mode
text: Score the following translation from {src_lang} to {tgt_lang} with respect to the human reference on a continuous scale from 0 to 100 that starts on “No meaning preserved”, goes through “Some meaning preserved”, then “Most meaning preserved and few grammar mistakes”, up to “Perfect meaning and grammar”.
{src_lang} source: “{source}” {tgt_lang} human reference: “{reference}” {tgt_lang} translation: “{translation}” Score (0-100):

heading: B AutoMQM Prompts with Four Input Modes

heading: T mode
text: Identify the major and minor errors in this translation. Note that Major errors refer to actual translation or grammatical errors, and Minor errors refer to smaller imperfections, and purely subjective opinions about the translation.
{tgt_lang} translation: “{translationi}” Errors: ...
{tgt_lang} translation: “{translation}” Errors:

heading: S-T mode
text: Based on the given source, identify the major and minor errors in this translation. Note that Major errors refer to actual translation or grammatical errors, and Minor errors refer to smaller imperfections, and purely subjective opinions about the translation.
{src_lang} source: “{sourcei}” {tgt_lang} translation: “{translationi}” Errors: ...
{src_lang} source: “{source}” {tgt_lang} translation: “{translation}” Errors:

heading: R-T mode
text: Based on the given reference, identify the major and minor errors in this translation. Note that Major errors refer to actual translation or grammatical errors, and Minor errors refer to smaller imperfections, and
purely subjective opinions about the translation.
{tgt_lang} human reference: “{referencei}” {tgt_lang} translation: “{translationi}” Errors: ...
{tgt_lang} human reference: “{reference}” {tgt_lang} translation: “{translation}” Errors:

heading: S-R-T mode
text: Based on the given source and reference, identify the major and minor errors in this translation. Note that Major errors refer to actual translation or grammatical errors, and Minor errors refer to smaller imperfections, and purely subjective opinions about the translation.
{src_lang} source: “{sourcei}” {tgt_lang} human reference: “{referencei}” {tgt_lang} translation: “{translationi}” Errors: ...
{src_lang} source: “{source}” {tgt_lang} human reference: “{reference}” {tgt_lang} translation: “{translation}” Errors:

heading: C Effects of Reference Quality
text: According to the results of WMT23 Metrics Shared Task (Freitag et al., 2023), poor human-generated reference translations can dramatically hurt the performance and reliability of the reference-based metrics. Here we perform a simple experiment to confirm this conclusion. We extract all of the samples whose MQM score of refA is less than or equal to - 2.0 from the WMT22 Zh-En test set, and finally get 5488 samples with 343 different sources Then we evaluate the performance of GEMBA-SQM-GPT3.5-turbo and GEMBA-SQM-Llama2-70B-Chat on this test set. The results are shown in Table 11. The gap between S-T and R-T/S-R-T gets much smaller. Sometimes S-T is even better than R-T. Consequently, we believe that the low-quality references have a negative impact on reference-based methods.

heading: D Shapley Values Calculation
text: We denote the meta-evaluation scores of each input mode as ST , SST , SRT and SSRT . The Shapley Value of the source part is
Shapleysrc = (SST − ST ) + (SSRT − SRT )
2 .
Similarly, the Shapley Value of the reference is
Shapleyref = (SRT − ST ) + (SSRT − SST )
2 .

heading: E Analysis by Log-Probability
text: We hypothesize that the non-trivial outcomes observed when employing the T mode may be attributed to the LLMs basing their scoring on the quality of the translation sentence provided that the translation is semantically similar to the source. We measure the quality of a sentence using logprobability. Moreover, drawing inspiration from generation-based methods, we also calculate the log-probability of the translation as a scoring metric when providing either the source, the reference, or a combination of both.
In this experiment, we only test the open models including both chat and base versions since the log-probability of ChatGPT is inaccessible at that time. We adopt the same prompt as above (Figure 1) for the chat models and just compute the vanilla log-probability of the translation part. As for the base models, considering they may be confused about the instruction, we only use the equal sign “=” to concatenate the source, reference, and translation sentences. For example, the prompt template of the S-R-T mode is “{source} = {reference} = {translation}”, and that of the T mode is simply the translation sentence “{translation}”. The log-probability of the translation sentence is computed as follows:
P (t) = N∑
i=1
log p(ti|c, t<i) (7)
where t is the tokens in the translation sentence of length N , and c is the context before the translation in the prompt, such as the instruction, the source, and reference information.
The test set, models, and metrics are identical to those used in the coarse-grained score prediction experiments, except that we add the base models.

heading: E.1 Results
text: As presented in Table 13, we have similar observations to the previous experiment. The superiority of the R-T mode is more prominent in this experiment, irrespective of the model type and size. This also corroborates that even powerful large language models cannot utilize the source information effectively in the translation evaluation task. The performance of the T mode which only computes the translation’s log-probability, remains significantly higher than random guess. The system-level accuracy of the T mode even exceeds the S-T and S-R-T mode by a large margin. These findings provide strong support for our hypothesis, suggesting that it is plausible for models to offer a relatively accurate score solely based on the quality of the translation sentence.
In this table, we also observe that the performance of each metric does not scale up well with the model size, regardless of further alignment. The system-level accuracy of models within the same base or chat series is comparable, with the 7B model even slightly outperforming the 70B model. Meanwhile, some of the segment-level correlations, like the correlations of T and S-T mode, are slightly increasing as the model size up. However, the slope is very gradual. We speculate that scaling may bring little benefit to the inherently deficient discriminate capability of auto-regressive language models, which is pertinent to the Generative AI Paradox (West et al., 2023).
When comparing Table 2 and Table 13, a peculiar phenomenon is observed that the segment-level correlations of log-probability are much higher than those of the score prediction method, whereas the system-level accuracy is significantly lower. We leave the reason behind as future work.

heading: F ChatGPT Token Usage
text: We record the ChatGPT token usage and cost in Table 12.

heading: Future Work
text: We leave the reason behind as future work