This podcast discusses the OpenAI paper “Why Language Models Hallucinate” by Adam Tauman Kalai, Ofir Nachum, Santosh S. Vempala, and Edwin Zhang.
It examines the phenomenon of “hallucinations” in large language models (LLMs), where models produce plausible but incorrect information. The authors attribute these errors to statistical pressures during both pre-training and post-training phases. During pre-training, hallucinations arise from the inherent difficulty of distinguishing correct from incorrect statements, even with error-free data.For instance, arbitrary facts without learnable patterns, such as birthdays, are prone to this.
The paper further explains that hallucinations persist in post-training due to evaluation methods that penalise uncertainty, incentivising models to “guess” rather than admit a lack of knowledge, much like students on a multiple-choice exam. The authors propose a “socio-technical mitigation” by modifying existing benchmark scoring to reward expressions of uncertainty, thereby steering the development of more trustworthy AI systems.
For the original article, click here.
