Papers by Aman Sinha
Language Models Learn Universal Representations of Numbers and Here’s Why You Should Care (2026.acl-long)
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Michal Štefánik, Timothee Mickus, Marek Kadlčík, Bertram Højer, Michal Spiegel, Raúl Vázquez, Aman Sinha, Josef Kuchař, Philipp Mondorf, Pontus Stenetorp
| Challenge: | Prior work has shown that large language models (LLMs) often converge to accurate input embedding for numbers, based on sinusoidal representations. |
| Approach: | They show that large language models often converge to accurate input embedding for numbers, based on sinusoidal representations. |
| Outcome: | The proposed representations are strikingly systematic, and are interchangeable in a large swathe of experimental setups. |
Your Model is Overconfident, and Other Lies We Tell Ourselves (2025.acl-long)
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| Challenge: | Analyzing 29 models, we find that difficulty is not linear or monotonic. |
| Approach: | They examine the interplay and divergence among various metrics for assessing intrinsic difficulty, including annotator dissensus, training dynamics, and model confidence. |
| Outcome: | The proposed model is based on 29 models on three datasets and analyzed by a linguistics team. |