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cs.CL2024
Distributional reasoning in LLMs: Parallel reasoning processes in multi-hop reasoning
Yuval Shalev, Amir Feder, Ariel Goldstein
Large language models (LLMs) have shown an impressive ability to perform tasks believed to require thought processes. When the model does not document an explicit thought process,…
cs.CL2024★ 2 cited
Can LLMs Learn Macroeconomic Narratives from Social Media?
Almog Gueta, Amir Feder, Zorik Gekhman +2
This study empirically tests the hypothesis, which posits that narratives (ideas that are spread virally and affect public beliefs) can influence eco…
cs.CL2024★ 14 cited
Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations?
Zorik Gekhman, Gal Yona, Roee Aharoni +4
When large language models are aligned via supervised fine-tuning, they may encounter new factual information that was not acquired through pre-training. It is often conjectured th…