Papers with LLM learning
Biasless Language Models Learn Unnaturally: How LLMs Fail to Distinguish the Possible from the Impossible (2026.eacl-long)
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| Challenge: | linguists have discovered patterns which hold across virtually all known natural languages . lingulists are able to learn languages by comparing their learning curves to those of humans . |
| Approach: | They compare LLM learning curves on existing and "impossible" datasets . they find that GPT-2 learns each language and its impossible counterpart equally easily . |
| Outcome: | The proposed model learns each language and its impossible counterpart equally easily, the study shows . the study also shows that the proposed model does not provide any kind of separation between the possible and the impossible . |
Mind the (Belief) Gap: Group Identity in the World of LLMs (2025.findings-acl)
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| Challenge: | Social biases and belief-driven behaviors can significantly impact Large Language Models’ (LLMs) decisions on several tasks. |
| Approach: | They propose a multi-agent framework that simulates belief congruence, a group psychology theory that plays a crucial role in shaping societal interactions and preferences. |
| Outcome: | The proposed framework reduces misinformation dissemination and improves learning by 11% while reducing misinformation dissemination by up to 37%. |
No Need for Explanations: LLMs can implicitly learn from mistakes in-context (2025.emnlp-main)
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| Challenge: | Existing literature assumes that correct answers to large language models must be accompanied by comprehensive rationales to be helpful. |
| Approach: | They propose to show incorrect answers to Large Language Models (LLMs) as a popular strategy to improve their performance in reasoning-intensive tasks. |
| Outcome: | The proposed approach outperforms chain-of-thought prompting in math reasoning tasks. |