Papers by David Alvarez-Melis
Investigating the interaction of linguistic and mathematical reasoning in language models using multilingual number puzzles (2025.emnlp-main)
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| Challenge: | Across languages, numeral systems vary widely in how they construct and combine numbers. |
| Approach: | They conduct experiments to examine the linguistic and mathematical aspects of numbers in language. |
| Outcome: | The models can't solve linguistic-mathematical puzzles involving cross-linguistic numeral systems, the authors found . they lack the ability to flexibly infer compositional rules from implicit patterns in human-scale data. |
Gromov-Wasserstein Alignment of Word Embedding Spaces (D18-1)
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| Challenge: | Current unsupervised methods for learning cross-lingual correspondences involve multiple steps, including heuristic post-hoc refinement strategies. |
| Approach: | They propose to cast the correspondence problem directly as an optimal transport problem, building on the idea that word embeddings arise from metric recovery algorithms. |
| Outcome: | The proposed method can be estimated efficiently, requires little or no tuning, and performs comparable with the state-of-the-art in various unsupervised word translation tasks. |
Data Drives Unstable Hierarchical Generalization in LMs (2025.emnlp-main)
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| Challenge: | Early in training, LMs can behave like n-gram models but eventually learn tree-based syntactic rules and generalize out of distribution (OOD). |
| Approach: | They study how complex data drives hierarchical rules, while less complex encourages shortcut learning . they find a model uses rules to generalize if its training data is *diverse* . |
| Outcome: | The proposed model learns to generalize hierarchically if its training data is complex . a model learn if it includes center-embedded clauses, a special syntactic structure . |