Automatically Suggesting Diverse Example Sentences for L2 Japanese Learners Using Pre-Trained Language Models (2024)

Abstract: Providing example sentences that are diverse and aligned with learners’ proficiency levels is essential for fostering effective language acquisition. This study examines the use of Pretrained Language Models (PLMs) to produce example sentences targeting L2 Japanese learners. We utilize PLMs in two ways: as quality scoring components in a retrieval system that draws from a newly curated corpus of Japanese sentences, and as direct sentence generators using zero-shot learning. We evaluate the quality of sentences by considering multiple aspects such as difficulty, diversity, and naturalness, with a panel of raters consisting of learners of Japanese, native speakers – and GPT-4. Our findings suggest that there is inherent disagreement among participants on the ratings of sentence qualities, except for difficulty. Despite that, the retrieval approach was preferred by all evaluators, especially for beginner and advanced target proficiency, while the generative approaches received lower scores on average. Even so, our experiments highlight the potential for using PLMs to enhance the adaptability of sentence suggestion systems and therefore improve the language learning journey.

heading: 1 Introduction
text: The term second language acquisition (or L2 acquisition) refers to the process of learning a second language by those who already know a first one. While children have a natural predisposition for acquiring languages, the degree of success among L2 learners varies greatly, as it is usually harder in adult life, requiring a combination of conscious effort, motivation, support from teachers and adequate materials (Fromkin et al., 2013).
Online dictionaries are usually the first resource towards which learners turn to in order to understand an unknown word or expression via definitions and example sentences. However, producing
*Research conducted during internship at NII, Japan.
high-quality learning material requires effort and expert knowledge. Because of that, researchers have explored automated techniques for selecting and generating examples to aid professionals like lexicographers or teachers, as well as non-experts like language learners (Kilgarriff et al., 2008; Ward, 2017; Pilán et al., 2013a).
Pre-trained Language Models (PLMs) have been shown to be effective for many NLP tasks (Wang et al., 2023). The main motivation for this work is to investigate whether PLMs can be leveraged to propose sentences that are understandable and diverse to help L2 learners be exposed to a broad range of uses for the target words they are inter-
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ested in (e.g. an unknown word encountered while reading), since examples contribute to improving vocabulary knowledge (Baicheng, 2009).
In this study, we focus on Japanese, as an increasing number of people are interested in achieving a certain level of proficiency, be it for study, work, culture or other reasons (Nakamachi et al., 2022). While there is substantial work on obtaining high-quality text from corpora or generative models, as discussed in Section 2, to the best of our knowledge, there are few studies simultaneously addressing the Japanese example sentence suggestion task, and developments in Natural Language Processing (NLP) such as the emergence of PLMs. The existing work mostly focuses on functional expressions (Liu et al., 2018a,b; Liu and Matsumoto, 2016; Shortt, 2021) or exercises (Andersson and Picazo-Sanchez, 2023).
Our contributions are summarized as follows: 1. We develop a retrieval-based approach to se-
lect example sentences from a corpus, by combining different PLM modules and NLP techniques for scoring sentence quality according to four criteria: difficulty, sense similarity, syntactic and lexical diversity. 2. We build WJTSentDiL, a corpus of sentences from different web sources, annotated with Japanese Language Proficiency Test1 (JLPT) labels. 3. We evaluate the quality of selected example sentences for specific target words by comparing the retrieval approach to two generative PLM baselines, employing native speakers and learners, alongside GPT-4 (OpenAI, 2023). We present the insights obtained from the investigation. The main repository for this work can be found here: NihongoExamplePLM.

heading: 2 Related Work
text: In the following we discuss the related work, namely retrieving and generating example sentences, and estimating sentence difficulty.
Example selection Similarly to Tolmachev and Kurohashi (2017), we seek to provide high-quality and diverse example Japanese sentences. They propose a thorough retrieval approach based on quality and diversity scoring using a Determinantal Point Process, and carry out an evaluation with L2 learn-
1More details on the JLPT website and Section 4.1.1.
ers and a teacher. Our work differs from theirs in that we focus on selecting sentences for sense similarity given a target word in context, instead of many possible senses for a word in isolation. Furthermore, we evaluate more aspects of the systems, in particular their capacity to adapt to learner proficiency levels. We also employ a language model in the evaluation.
Many other works deal with the task of example sentence selection from a corpus, focusing on dictionary examples for English, Japanese and Swedish (Kilgarriff et al., 2008; de Melo and Weikum, 2009; Hazelbeck and Saito, 2009; Pilán et al., 2013b). Additionally, Shinnou and Sasaki (2008), Kathuria and Shirai (2012) and Cheng et al. (2018) leverage parallel corpora to extract disambiguated sentences, while we limit our experiments to the monolingual setting.
Example generation There is a lot of research on controllable text generation approaches (Zhang et al., 2023a). Possible generation targets are definitions for a given term (Zhang et al., 2023b; Gardner et al., 2022), as well as example sentences. When it comes to example generation, researchers have shown that generated sentences can improve performance in Word Sense Disambiguation tasks in a supervised (Barba et al., 2021) or unsupervised way (He and Yiu, 2022). Focusing on L2 learners, Harvill et al. (2023) consider lexical complexity and sentence length to generate example sentences of controllable difficulty. In our case, we opt not to rely on fixed sense inventories, primarily due to the scarcity of available sense-tagged corpora. However, we believe that assigning dictionary definitions to words could prove beneficial to learners.
Sentence difficulty estimation Determining the level of difficulty of text is a key challenge in educational NLP, as vocabulary and grammatical structure interact in a complex way (Collins-Thompson, 2014). To estimate the difficulty of Japanese sentences, Nakamachi et al. (2022) show that a BERTbased classifier (Devlin et al., 2019) trained on labeled examples can achieve good performance, surpassing existing readability metrics2 and approaches based on word frequencies. Liu and Matsumoto (2017) focus on estimating Japanese text difficulty for learners with pre-existing knowledge of Chinese characters. In that case, the main source of difficulty is not vocabulary, but grammar and
2https://jreadability.net/sys/en
functional expressions. In our work, due to lacking training data from official JLPT material, we train a similar classifier to Nakamachi et al. (2022).

heading: 3 Task: Example Sentence Suggestion
text: We define the L2 contextualized example suggestion task as:
M(w, s0, d) = {s1, s2, . . . , si, . . . , sK} (1) Given a target word w, a context sentence s0 and a target difficulty level d, we want to obtain a list of K good example sentences from a model M .
To expand more on what makes a good example, Kilgarriff et al. (2008) suggest that such examples should represent typical usage, be informative and understandable to learners. Building upon the discussion presented by Tolmachev et al. (2022), we aim to obtain multiple examples with diverse syntactic patterns since learners preferred them.

heading: 4 Methodology

heading: 4.1 Retrieval method
text: We design a retrieval model that, given a query, will select candidate sentences containing a target word from a corpus and present them to the learner (for more details on the corpus, see Section 5.1). Candidate sentences are ranked by how closely they match the target difficulty level and the semantic similarity of the target word in both the suggested and context sentences. Finally, the model selects a subset of sentences considering the total diversity of the list. In summary, we devise a model to quantify for a sentence si:
1. how adequate si is with respect to the target difficulty level d (Sec. 4.1.1). 2. if si contains the target word w and it is used in the same sense as the target word of the context sentence (Sec. 4.1.2). 3. the diversity of {s0, s1, s2, . . . , si, . . . , sK} on vocabulary and syntax (Sec. 4.1.3).

heading: 4.1.1 Quality: difficulty
text: The Japanese Language Proficiency Test (JLPT) has a proficiency scale similar to the Common European Framework of Reference for Languages (CEFR). The JLPT levels are, from easier to harder: N5, N4, N3, N2 and N1. Our classifier will therefore assign a JLPT level di to input sentences. Then, it will be mapped to a difficulty score between 1 and 0. We formulate this score as
max ( 0, 1− penaltydiff ∗ (d− di) ) (2)
where d and di are the target difficulty level and difficulty label of the sentence i. We manually set the coefficient penaltydiff to 0.2. We increase the coefficient to 0.4 on sentences deemed harder than the target level because L2 learners might benefit more from easier sentences in case of discrepancies.

heading: 4.1.2 Quality: sense similarity
text: Pilehvar and Camacho-Collados (2019) propose Words in Context (WiC), a different declination of Word Sense Disambiguation. WiC is a binary classification task: given a target word and two contexts, the model has to predict whether the word is used with the same meaning. Since we also tackle this problem in our case, we turn to MirrorWiC, an unsupervised fine-tuning method for contextualized word sense embeddings (Liu et al., 2021a). We fine-tune a PLM with MirrorWiC and use the resulting model to extract a vector representation for the target words in context. Then, we assign a sense similarity score based on cosine similarity between s0, the context sentence, and si.

heading: 4.1.3 Diversity: syntactical and lexical
text: Inspired by the way Tolmachev and Kurohashi (2017) measured syntax diversity, we opt for a simpler approach, supported by other works on syntax similarity (Chen et al., 2023a; Kanagawa and Okadome, 2016).
We compute dependency trees of two sentences and partially generalize their labels, then apply a Label-based Tree Kernel Similarity method, FastKASSIM, to obtain a diversity score (Chen et al., 2023a; Moschitti, 2006; Boghrati et al., 2018). More in detail, we compute the parse trees and the number of shared subtrees of a pair of sentences. The latter is normalized with the square root of the product of the number of subtrees for each sentence (Chen et al., 2023a). For the syntactic diversity of a list of sentences, we take the average of pairwise scores.
For lexical diversity, we simply compute the average percentage of unique 1-2-3-4-grams in a sentence list.
Finally, we obtain a combined diversity score by equally weighting the lexical and syntax scores.

heading: 4.1.4 Ranking and Greedy Selection
text: As the number of candidates can be very high, we greedily select K final sentences. First, we sort the candidate sentences in terms of difficulty and sense scores, having equal weights as we consid-
ered the qualities equally important for this experiment. Then, within a window, we iteratively add the sentence which achieves the highest diversity score, until the list is complete. We set a window of only 50 candidates in the preliminary experiments. Otherwise, queries would take a long time due to having to re-compute similarity scores for every partial list.

heading: 4.2 PLM generation method
text: Considering the PLM baselines, we prompt them with the query, expressed in English. We share the prompt used in Appendix C. As initial experiments revealed that complying with the query in zeroshot manner was quite difficult, we prompt the PLMs multiple times, concatenate the outputs and exclude duplicates and sentences without the target word, until we get the required number of sentences. In the majority of cases, twice was enough. We set the generation temperature parameter to 1.0 for all PLMs; additionally, for LLM-jp, we add a repetition penalty of 5.0.

heading: 5 Experimental setup

heading: 5.1 Dataset: WJTSentDiL Corpus
text: We present WJTSentDiL,3 a corpus of Wikipedia, JpWaC and Tatoeba Sentences with Difficulty Level. It is built by merging together three public corpora (described below) and performing additional filtering to remove spurious sentences. Additionally, our difficulty classifier adds JLPT levels to each sentence.
• Tatoeba is a platform where users can share sentences and translations. We select only Japanese sentences and fix errors where entries are made from multiple sentences. • JpWaC (Sangawa et al., 2010) is a curated corpus of sentences automatically collected from Japanese web domains. We include subsets L0 to L4 of the corpus. • Wikipedia is a free online encyclopedia. We process raw article text from the Japanese part of the website, more specifically the “jawiki dump” from December 2023.
We use spaCy4 and Ginza5 to split raw text into sentences, tokenize them, and assign part-ofspeech (POS) tags. To keep well-formed sentences,
3The corpus is available on HuggingFace. 4Repository for spaCy, version 3.7.2 5Repository for ginza, version 5.1.3, ‘ja-ginza’ model.
we apply filters following heuristics similar to Kilgarriff et al. (2008) and Sangawa et al. (2010). Namely, we keep sentences that:
• have a length between 5 and 50 tokens. • have less than 20% punctuation or numerals. • do not contain tokens from the Latin, Cyrillic
and Arabic scripts. • end in a predicate and punctuation, or particles
such asよ,ね. • are not duplicates.
Wikipedia sentences are what makes up most of the corpus. They are on average longer and contain more kanji, Chinese characters, compared to the other sources. We show statistics in Table 1.

heading: 5.2 Retrieval method details

heading: 5.2.1 Inverted index
text: The retrieval model uses an inverted index, mapping words to sentences they appear in. The keys are lemmas or “dictionary forms” of words and compound nouns. The candidate sentences are retrieved using the index by lemmatizing the target word. For example, the target word “たべた” (past form of to eat) is lemmatized as “食べる+た” (to eat + past tense auxiliary verb).

heading: 5.2.2 Difficulty classifier
text: The JLPT difficulty classifier is a BERT model pretrained on texts in the Japanese language,6 that we fine-tuned on 5,000 sentences from Japanese language learning websites.7 Their labels are assigned based on HTML metadata specific to each website. For more details on the training and evaluation of the classifier, see Appendix A. Its performance is very good (84% accuracy) on in-distribution data (i.e. the validation split), but it worsens on a different test set composed of official JLPT past exam sentences (38% accuracy). Our hypothesis is that
6tohoku-nlp/bert-base-japanese-v3 7nihongokyoshi-net.com, jlptsensei.com. Due to license limitations, we can not share the sentences, but the model is available on HuggingFace.
the latter test set contains very long sentences composed of many relative clauses, which are very different from the sentences used for training.

heading: 5.2.3 Sense embeddings
text: We use MirrorWiC (Liu et al., 2021a) to fine-tune multiple baseline PLMs with 10,000 sentences randomly chosen from our corpus. To guide model selection, we look at their performance on two WiC tasks, XL-WiC (Raganato et al., 2020) and AM2iCo (Liu et al., 2021b). MirrorWiC finetuning shows a small improvement on both tasks for BERT-base-japanese, over the same base model and a Japanese Sentence Transformer.8
To obtain the embeddings, we average the last 4 layers of the embedding model, and across the sub-tokens that make up the target word, following Liu et al. (2021a).

heading: 6 Evaluation

heading: 6.1 Goals of the evaluation
text: We outline the core research questions that guide our investigation. Q1: The capabilities of LLMs such as GPT-4 in
rating text have been explored (Chen et al., 2023b). Therefore, can GPT-4 evaluate the quality of Japanese sentences from the perspective of L2 learners, and how do its assessments compare to those given by humans?
Q2: How do the automated quality metrics we used to guide the development of the retrieval approach compare with human judgment? Q3: How good are PLMs at following instructions for this complex task? Q4: Is text retrieved from a corpus (assumed to be human-authored) preferred to generated text? Q5: What do humans think of their output? We try to answer those questions by asking volunteer L2 learners and Japanese native speakers to manually rate and rank systems outputs.

heading: 6.2 Selected baselines
text: The systems we consider are the retrieval approach (Section 4.1), LLM-jp, a Japanese PLM,9 and GPT3.5. Specifically, throughout the paper, when mentioning GPT-3.5 we mean GPT-3.5-turbo-0613, while GPT-4 is GPT-4-0125-preview.
8sonoisa/sentence-bert-base-ja 9llm-jp/llm-jp-13b-instruct-full-jaster-dolly-oasst-v1.0

heading: 6.3 Evaluation data preparation
text: We build a set of target words from those used in the human evaluation of Tolmachev and Kurohashi (2017) and also add words from a work in WSD by Okumura et al. (2011). The former study involved 14 target words, and the latter 50, sharing one word, resulting in a total of 63. We randomly divided them into 53 for validation and experimental use, and 10 for testing and human evaluation, but ensuring a test set composition of 3 nouns, 4 verbs, 2 adjectives, and 1 adverb.
In addition, for every target word, we obtain a context sentence by randomly selecting sentences from yourei and gogo,10 websites which provide a search engine for snippets of text content.

heading: 6.4 Human evaluation guidelines
text: We consider as a query the input for the task (Equation 1), namely the selected word for human evaluation, along with their associated context sentence and target level. In this experiment we target levels N1, N3 and N5. The system outputs are randomly ordered and presented with the query, forming an “annotation block”. Each baseline provides K = 5 sentences. This results in 30 blocks (10 queries × 3 levels), and 150 sentences for each system (30 blocks × 5 output sentences).
We ask evaluators to rate: 1. Difficulty level, by rating the difficulty of
each sentence on the JLPT scale. This is to see how closely systems match the target difficulty. 2. Sense similarity, by evaluating whether the usage of the target word in each sentence aligns with its sense in the original context. This is to see whether the proposed sentences retain the use of the word in a similar sense. 3. Rejection: sentences should be marked for rejection if they are deemed not useful (e.g. unnatural usage) or confusing (e.g., grammatical or segmentation errors). 4. Syntactic Diversity, by examining the variety in sentence structure and the different grammatical constructions used to incorporate the target word. 5. System Ranking: after rating each system’s outputs, rank them from best to worst. The ranking should consider the overall utility for language learners at the target proficiency.
10https://yourei.jp, https://dictionary.goo.ne.jp
We demonstrated the task and explained the evaluation guidelines. The total participants are 5, of which 3 are native Japanese speakers and 2 are learners of proficiency N1-N2. Addionally, we include an annotation block example in Appendix B.

heading: 6.5 PLM evaluation protocol
text: We feed GPT-4 a modified version of the evaluation guidelines, the system outputs, and ask it to rate them. More details can be found in Appendix D. Empirically, we noticed that ratings for the same prompt sometimes were different, even when trying to reduce variability. So, we query GPT-4 three times, and also obtain its majority vote. We note that in some cases this could still result in an unclear rating.

heading: 7 Results and Discussion
text: In this section, we present the results from the evaluation. We try to address our research questions in three main parts: agreement between raters; systems comparison; comments and error analysis.

heading: 7.1 Q1: Agreement of ratings
text: The Intraclass correlation coefficient (ICC) is a widely used statistical measure for reliability, that reflects the degree of correlation and agreement between ratings (Koo and Li, 2016). The reason for choosing this metric is that it takes into account the magnitude of the differences between scores. For example, it is important that if a sentence is rated N1 by one person and N5 by another, it is seen as a larger disagreement than one rated N1 and N2.
We compute the metric with the pingouin library,11 and we convert ratings from ordinal labels into numbers, mapping them in a scale where the relative distances are the same among labels. Following Hackl et al. (2023), who studied the reliability of GPT-4 in a similar experiment, we use a specific ICC setting based on a two-way mixed effect model. In short, ICC(3,1), according to the naming convention of Shrout and Fleiss (1979).

heading: 7.1.1 GPT-4 rating consistency
text: In Table 2, we report ICC values for the quality ratings across groups of raters. We include in this table only raters who compiled at least half of the blocks for each target level, in order to have a generalizable idea of the agreement.
11https://pingouin-stats.org/build/html/index.html
For GPT-4, despite setting its behavior to be nearly deterministic and obtaining ratings on the same day, we observed that the consistency of its ratings varies by type. The model shows excellent agreement in assessing JLPT levels and good consistency in rejecting sentences. However, its consistency is lower for other evaluation areas like sense similarity, syntax diversity, and model ranking. Using a mean combination of ratings improves consistency, but comes at the cost of more forward passes on the same long inputs. A way to further mitigate this is improving the prompt.

heading: 7.1.2 Agreement among groups
text: Focusing on human raters, it seems that agreement on qualities except difficulty level is quite low (Table 2). One reason for this could be that the guidelines for other metrics are too generic, which causes more variability in the ratings. However, we expected that language learners and native speakers may not have the same rating patterns. Additionally, since we required many ratings at once, there could be some additional effects at play, such as fatigue or bias from the order of annotation.

heading: 7.1.3 Pairwise agreement on ranking
text: To further investigate whether GPT-4 ranks similarly to humans, in Table 3 we report the pairwise agreement for the preferred system ranking from all annotators.
Inter-rater agreement between GPT-4 and humans is generally lower than those among humans of different groups. This suggests that humans, regardless of whether they are native speakers or not, have more similar ranking preferences compared to the AI models. However, there are also outliers, such as HN2, who has a way of ranking that shows no agreement with many other raters. This highlights the challenge in aligning AI evaluations with human preferences and confirms that, even among humans, there is significant disagreement on judging learning material suitability.

heading: 7.2 Q2-3-4: Quantitative analysis of ratings
text: After the agreement analysis, we discuss how raters evaluated the systems. For qualities other than difficulty and ranking preference, we report the main empirical findings in the following, and release additional figures in Appendix E.

heading: 7.2.1 Difficulty level ratings
text: Figure 2 shows the proportion of human-assigned JLPT difficulty labels for each system, grouped by
target level. When considering how close the difficulty of proposed sentences is to the target level, our retrieval approach is markedly better for N1 and N5, while for N3, it produced a significant proportion of harder sentences. GPT-3.5 seems better for N3, but being so consistent is not always an advantage because it makes it difficult to adapt to user requirements, for example when requesting advanced sentences. LLM-jp also had issues following the prompt: repetitions, sentences without the target word, incoherent text.

heading: 7.2.2 Sense similarity ratings
text: When the raters indicated whether the target word in each sentence had a similar meaning as the one in the context, the vast majority classified the sense as being the same. The percentage of sentences rated as “not similar” was only about 2% for the retrieval,
and 13% for the generative baselines. This shows that the systems generally succeed in producing examples with similar nuances.

heading: 7.2.3 Rejection ratings
text: According to our evaluation guidelines, unnatural sentences and those with confusing errors should be marked. On average, 8% of sentences suggested by the retrieval were rejected, while for LLM-jp it was 13%, and 16% for GPT-3.5.
Checking raters’ comments confirmed that there were some segmentation errors in retrieval and generation baselines, such as sentences starting with punctuation, or with a fragment. It seems that generative models are more prone to errors, while the retrieved sentences are better in this aspect “by design”. Still, careful text pre-processing and postprocessing is needed as sentences with errors can
be confusing for beginner learners. For a couple of concrete examples, the following sentence from the retrieval approach was rejected by some human participants because it sounded unnatural and too literary: この闘いは今日の場 合では大概は容易ならぬ苦闘だからだ。 “As for this fight, in today’s situation, it is generally a difficult struggle”.
Finally, the following was generated by LLM-jp and was rejected because of the presence of confusing characters and English words at the beginning: favorite dish is sushi.1.右手で持ってい たスプーンを左手でも持てるようになったん だ。 “The spoon that I was holding with the right hand, I became able to hold with the left hand as well”.

heading: 7.2.4 Diversity ratings
text: Considering the syntax diversity of the list of sentences, the retrieval method earned the most “high”
ratings across all target levels. GPT-3.5 received mostly “medium” votes, and LLM-jp got the lowest. The latter model often produced repetitive sentences, where only one or two words would differ between each generated sentence. This highlights another issue in zero-shot generation, i.e. that it is difficult to have both diversity and adherence to instructions.

heading: 7.2.5 System ranking ratings
text: Table 4 presents votes on system ranking by human participants and GPT-4. The sentence lists produced by the retrieval system are the best overall for all raters when considering the total vote count. Except for HL2 and HN3, the retrieval system is rated best in over 50% of cases. When considering target levels, it also markedly wins in suggesting lists for advanced and beginner target difficulty levels, while it is not rated best as much for the intermediate level. The sentences suggested by
the retrieval system for N3 are often on the more difficult end, as shown in Figure 2.

heading: 7.3 Q5: Qualitative analysis and participants comments
text: A native speaker commented on a target word in the evaluation (全然, zenzen). It is commonly used in negative statements, to mean “not at all” (Sawada, 2007). Using it in positive statements can be considered “slightly broken” in formal situations, but it was correct a hundred years ago, and it is used in today’s slang. In that case, GPT-3.5 produced a similar sentence as the context in which the usage was “uncommon”. Indeed, the context sentence was from an excerpt of a work published in 1938 by Osamu Dazai, a famous Japanese writer. This should prompt thinking about what actually makes a correct sentence. Language learners noted that many sentences contained one or two difficult kanji, encountered at higher proficiency levels, even though the overall sentence structure is more straightforward to understand. This happened mostly with the retrieval approach, which did not take word difficulty explicitly into account.

heading: 8 Conclusion
text: This paper outlines a methodology for suggesting example sentences to learners of Japanese. It is adaptable to other languages with minor adjustments. The baselines we consider highlight many possible roles of PLMs: assessing difficulty, encoding semantic representations, directly producing sentences and evaluating their quality, all of which could be investigated further on the basis on their applicability in AI-supported language learning and other fields in education technology.
From the feedback and data collected from the human evaluation, we can point out the potential for improving and combining these systems to balance their shortcomings, even though the retrieval methodology was considered to be the best in terms of diversity and adherence to difficulty level.
The challenge of evaluating generated text prompted us to explore a state-of-the-art LLM’s ability in rating sentence quality. In our opinion, it is a promising direction because the model seems to be able to evaluate linguistic features of sentences. We found good agreement in rating text difficulty, but since each person could make different assessments, finding a way to take that variability into account could be useful for personalization.
It could be studied whether using word-level features can prevent unknown kanji from appearing in example sentences. Such features could be JLPT labels or the school grade level they are taught in. Another research challenge is estimating the real vocabulary known by the learner, modeling the process of second language acquisition (Settles et al., 2018; Cui and Sachan, 2023). Additionally, there is potential for suggestion and generation of material based on each learner’s interests.
A direction to explore further is to experiment with more advanced LLM prompting strategies, such as chain of thought or reinforcement learning, to iteratively refine outputs for better adaptation to learners’ preferences. A retrieval approach like ours could serve as a starting point.
Limitations
In our work, the retrieval approach scores sentences using mainly unsupervised approaches and PLMs.
The corpus we build is not as large as other corpora. In our comparisons, for LLMs we explored only basic prompting strategies without fine-tuning, wanting to investigate approaches in a setting without labeled data.
As for the evaluation, the number of volunteers who participated in the study was quite limited and the agreement values are not very high, indicating that the results are not generalizable to larger groups. Nevertheless, we believe that the feedback and guidelines could be valuable for future research. About half of them were foreign students, and their feedback was valuable. Unfortunately, due to lack of resources, none of the native speakers were language educators. Involving language teachers would be advisable. Additionally, comparing our baselines with the approach of Tolmachev and Kurohashi (2017) would have been insightful. However, due to the absence of a practical implementation and limited resources for human evaluation, we opted for PLM baselines.
Ethics Statement
Because of the training methods of base LLMs, sentences generated or retrieved using these approaches could reflect negative biases that could impact or influence negatively the model of language that is internalized by the learners. It poses an increased risk when there are not enough sources of information, or limited sharing of ideas and communication with other learners and native speakers
of the foreign language that can more effectively teach distinguishing polite and casual register and other aspects of pragmatics, other than just word usage.

heading: Acknowledgements
text: This work was supported by JSPS KAKENHI Grant Number 24K03231. We thank the LLM Research and Development Centre for LLMs, National Institute of Informatics, Japan, for the support.
We also wish to thank the volunteer participants in the evaluation experiments for their help, Dr. Arseny Tolmachev for the precious advice, and the anonymous reviewers for their feedback.

heading: A Difficulty classifier training and evaluation
text: B Human evaluation form - Example of evaluation block

heading: C LLM baselines prompts
text: We share the prompts, obtained with manual testing and trial and error. We found that the models responded in a satisfactory way also to prompts where the request was formulated in plain English, as well as in Japanese.
For LLM-jp, this was the prompt used to obtain the final outputs:
write k target level example sentences in japanese, that must contain the word "target word" used in a similar sense as "context sentence". following are k diverse sentences that must use "target word":
For GPT-3.5, we used the same prompt as the other LLM, and only appended the following instruction to reduce verbosity.
Provide sentences in Japanese in a numbered list, without any translation or romaji.

heading: D GPT-4 evaluation prompt
text: We present the prompt given to GPT-4 when rating evaluation blocks with the baselines outputs:
This evaluation aims to rate and compare three systems in providing good example sentences for learners of Japanese at different proficiency levels. An annotation block consists of proposed sentences by 3 systems for a target word, a context sentence and a target difficulty level. The lists of sentences are supposed to help language learners to see diverse examples of a target word in context.
Difficulty: Rate the difficulty of each sentence according to the JLPT (Japanese Language Proficiency Test) scale, where N1 is the most difficult and N5 is the easiest. Indicate which level a sentence belongs to (one of N1, N2, N3, N4, N5). It is possible that for the target level, the system proposes a sentence that
is of a different level (higher or lower). Below is a summary of the proficiency levels.12
Level Description
N1 Complex and abstract Japanese across various contexts. N2 Everyday Japanese in varied situations, with clear materials on different topics. N3 Japanese in common everyday situations. N4 Basic Japanese understanding, including familiar topics, basic vocabulary, and kanji. N5 Fundamental Japanese, including hiragana, katakana, and basic kanji.
Sense Similarity: Indicate if the target word in each sentence maintains a close sense as in the original context. Possible values: "similar", "not similar". Think broadly and intuitively, rather than strictly by dictionary definitions.
Reject: For each sentence, indicate "Reject" if you think the sentence is not good or useful (for example because it does not reflect natural use).
Sentence diversity: For each system output list, rate the sentences diversity, focusing on the amount of different uses of syntax and structure. Possible values: "Low", "Medium", "High".
System ranking: Rank the systems’ outputs from best to worst, considering the overall usefulness of the example sentences for that word, for a language learner of that proficiency level.
Comment: Leave a short comment.
12Taken from https://www.jlpt.jp/e/about/levelsummary.html. The description are put into a table for readability.

heading: E Additional rating statistics

heading: Future Work
text: Nevertheless, we believe that the feedback and guidelines could be valuable for future research. About half of them were foreign students, and their feedback was valuable. Unfortunately, due to lack of resources, none of the native speakers were language educators. Involving language teachers would be advisable. Additionally, comparing our baselines with the approach of Tolmachev and Kurohashi (2017) would have been insightful. However, due to the absence of a practical implementation and limited resources for human evaluation, we opted for PLM baselines