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cs.CL2025
AbstRaL: Augmenting LLMs' Reasoning by Reinforcing Abstract Thinking
Silin Gao, Antoine Bosselut, Samy Bengio +1
Recent studies have shown that large language models (LLMs), especially smaller ones, often lack robustness in grade school math (GSM) reasoning. In particular, they tend to experi…
cs.CL2025
What Makes the Preferred Thinking Direction for LLMs in Multiple-choice Questions?
Yizhe Zhang, Richard Bai, Zijin Gu +5
Language models usually use left-to-right (L2R) autoregressive factorization. However, L2R factorization may not always be the best inductive bias. Therefore, we investigate whethe…