of memory we provided. The training process is akin to giving a person 2,000 poems and asking them to memorize as many as possible, with a limit of reading each poem only 100 times. In the CN Poems dataset, the best-performing models were Qwen2-1. 5B-Instruct and bloom-1b4-zh, which remembered 1,938 and 1,932 poems respectively. In contrast, for the ENG Poems dataset, nearly all models memorized all the poems. These results are remarkable. A human, without specialized memory training, would struggle to memorize even 100 poems under such conditions, whereas the LLMs were able to memorize almost 100% of the 2,000 poems. However, overall performance on the CN Poems dataset was weaker for models like Qwen1. 5-0. 5B-Chat, Qwen2-0. 5B-Instruct, and bloom-389m-zh. We believe this is due to insufficient pre-training, resulting in relatively poor language comprehension. For instance, Qwen2-0. 5B-Instruct outperformed Qwen1. 5-0. 5B-Chat, even though both models had similar data and model sizes. Stronger language understanding suggests a better fit for natural language, which can be interpreted as a model having a higher language comprehension ability. This, in turn, aids in model expansion. The original Qwen documentation supports this, as Qwen2-0. 5B-Instruct outperforms Qwen1. 5-0. 5B-Chat across a variety of tasks. Similarly, a person with better language comprehension would learn other related information more efficiently. The availability and comprehensiveness of English-language data compared to Chinese likely contributed to the models' superior performance in fitting English texts. Another factor may be that Chinese is a more complex language, and smaller models have limited learning capacity. In larger models such as Qwen2-1. 5B-Instruct and bloom-1b4-zh, performance improved significantly over Qwen2-0. 5B-Instruct and bloom-389m-zh, approaching the results seen in the ENG Poems dataset. Figure 2 shows the results of generating a full poem based on partial input information. As seen in the figure, after training, the models can successfully recite the entire poem using only the title and author. Figure 2 displays some examples of prediction errors. Input: Dictation: Dynasty: 宋代 Author: 李弥逊 Title: 题大儒寮小阁Model: Qwen1. 5-0. 5B-Chat Output: 青鞋踏尽剑鋩山，借枕僧房落照间。 高屋凭虚听泉语，岭云应似我身闲。 Input: Dictation: Dynasty: 唐代 Author: 岑参 Title: 故仆射裴公挽歌三首 Output: 盛德资邦杰，嘉谟作世程。门瞻驷马贵，时仰八龙名。 罢市秦人送，还乡绛老迎。莫埋丞相印，留著付玄成。 五府瞻高位，三台丧大贤。礼容还故绛，宠赠冠新田。 气歇汾阴鼎，魂飞京兆阡。先时剑已没，陇树久苍然。 富贵徒言久，乡闾殁后归。锦衣都未著，丹旐忽先飞。 哀挽辞秦塞，悲笳出帝畿。遥知九原上，渐觉吊人稀。Input: Dictation: Author:Michael Ryan Title: Larkinesque Model: Qwen1. 5-0. 5B-Chat Output: Reading in the paper a summary of a five-year psychological study that shows those perceived as most beautiful are treated dif ferently, I think they could have just asked me, remembering a kind of pudgy kid and late puberty, the bloody noses and wisecracks because I wore glasses, though we all know by now how awful it is for the busty starlet no one takes seriously, the loveliest women I've lunched with lamenting the opacity of the body, they can never trust a man's interest even when he seems not just out for sex (eyes focus on me above rim of wineglass), and who would want to live like this? And what does beauty do to a man?-Don Juan, Casanova, Lord Byron-those fiery eyes and steel jawlines can front a furnace of self-loathing, all those breathless women rushing to him while hubby's at the of fice or ball game, primed to be consumed by his beauty while he stands next to it, watching. So maybe the looks we're dealt are best. It's only common sense that happiness depends on some bearable deprivation or defect, and who knows what conflicts great beauty could have caused, what cruelties one might have suf fered from those now friends, what unmanageable possibilities smiling at every small turn? So if I get up to draw a tumbler of ordinary tap water and think what if this were nectar dripping from delicious burning fingers, will all I've missed knock me senseless? No. Of course not. It won't. Figure 2: The process of Transformer. 5