| Challenge: | a recent study suggests that language models perform poorly across languages. |
| Approach: | They propose a model that fits a paired-sample multiplicative mixed-effects model to obtain language difficulty coefficients from at least-pairwise parallel corpora. |
| Outcome: | The proposed model is able to handle missing data and is aware of inter-sentence variation. |
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Morphology Matters: A Multilingual Language Modeling Analysis (2021.tacl-1)
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| Challenge: | Existing studies on inflectional morphology disagree on whether or not it makes languages harder to model. |
| Approach: | They propose to use a corpus of 145 Bible translations in 92 languages to investigate whether inflectional morphology makes languages harder to model. |
| Outcome: | The proposed model trains with linguistically motivated subword segmentation strategies and reduces the impact of morphology on language modeling. |
Are All Languages Equally Hard to Language-Model? (N18-2)
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| Challenge: | a fair comparison of language models is tricky because of the size of the corpora and the variability of orthographic systems. |
| Approach: | They propose a framework for fair cross-linguistic comparison of language models . they show that in some languages, textual expression is harder to predict with n-gram models compared to LSTM models based on translated text . |
| Outcome: | The proposed framework is based on translated text and language models on 21 languages. |
Why do language models perform worse for morphologically complex languages? (2025.coling-main)
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| Challenge: | Language models perform differently across languages, a new study suggests . morphological typology may explain some of the performance differences, authors say . |
| Approach: | They propose to test morphological alignment of tokenizers, tokenization quality and disparities in dataset sizes and measurement to test this hypothesis. |
| Outcome: | The proposed model shows that fusional languages perform better than fusionative languages . the authors suggest that morphological typology may explain some of the performance differences . |
A Systematic Survey and Critical Review on Evaluating Large Language Models: Challenges, Limitations, and Recommendations (2024.emnlp-main)
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Md Tahmid Rahman Laskar, Sawsan Alqahtani, M Saiful Bari, Mizanur Rahman, Mohammad Abdullah Matin Khan, Haidar Khan, Israt Jahan, Amran Bhuiyan, Chee Wei Tan, Md Rizwan Parvez, Enamul Hoque, Shafiq Joty, Jimmy Huang
| Challenge: | Large Language Models (LLMs) have gained significant attention due to their capabilities in performing diverse tasks across domains. |
| Approach: | They review the primary challenges and limitations causing inconsistencies in evaluations . early models could generate coherent text but limited to simple tasks . |
| Outcome: | The proposed evaluations are reproducible, reliable, and robust. |
Overestimation of Syntactic Representation in Neural Language Models (2020.acl-main)
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| Challenge: | Several testing methodologies have been developed to probe models’ syntactic representations. |
| Approach: | They propose a method to determine syntactic structure by training a model on strings generated according to a template and testing its ability to distinguish between similar ones with different syntax. |
| Outcome: | The proposed method reproduces positive results with two non-syntactic baseline language models: an n-gram model and an LSTM model trained on scrambled inputs. |
Evaluating Large Language Models on Controlled Generation Tasks (2023.emnlp-main)
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Jiao Sun, Yufei Tian, Wangchunshu Zhou, Nan Xu, Qian Hu, Rahul Gupta, John Wieting, Nanyun Peng, Xuezhe Ma
| Challenge: | Recent studies have looked into the ability of large language models in various benchmark tasks, including question generation, reading comprehension, multilingual and etc. However, few studies investigate the controllability of large languages. |
| Approach: | They propose to compare large language models with state-of-the-start finetuned smaller models to find that large language model controls are comparable to smaller models. |
| Outcome: | The proposed model can meet hard constraints and perform better than state-of-the-art models. |
An Analysis of Massively Multilingual Neural Machine Translation for Low-Resource Languages (2020.lrec-1)
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| Challenge: | In this study, we explore massively multilingual low-resource neural machine translation. |
| Approach: | They propose to use Bible translations to train models with up to 1,107 source languages and create multilingual corpora varying the number and relatedness of source languages. |
| Outcome: | The proposed approach is highly language-specific and can be tailored to the source language and its typology. |
How Do Large Language Models Capture the Ever-changing World Knowledge? A Review of Recent Advances (2023.emnlp-main)
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| Challenge: | Large language models (LLMs) are impressive in solving tasks, but they can quickly be outdated after deployment. |
| Approach: | They provide a review of recent advances in aligning deployed large language models with the ever-changing world knowledge. |
| Outcome: | The proposed models can be used to perform various tasks directly through in-context learning or for further fine-tuning for domain-specific uses. |
The Less the Merrier? Investigating Language Representation in Multilingual Models (2023.findings-emnlp)
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| Challenge: | Multilingual models can be used to integrate multiple languages into one model and use cross-language transfer learning to improve performance for different NLP tasks. |
| Approach: | They propose to include languages in popular multilingual models and to use cross-language transfer learning to improve performance for different NLP tasks. |
| Outcome: | The proposed models perform better on downstream tasks for seen and unseen languages than community-centered models for low-resource languages. |
Methods for Estimating and Improving Robustness of Language Models (2022.naacl-srw)
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| Challenge: | Large language models suffer from weak generalisation ability due to shallow textual relations over full semantic complexity of the problem. |
| Approach: | They propose to incorporate some of these measures into training objectives to enhance distributional robustness of LLMs. |
| Outcome: | The proposed models outperform human models on complex tasks and outperformed other models on deep networks. |