Reproduce “Position: It's Time to Optimize LLMs for Self-Consistency” on OpenResearch

About this paper

Itamar Pres, Belinda Z. Li, Laura Ruis, Zifan Carl Guo, Keya Hu, Mehul Damani, Isha Puri, Ekdeep Singh Lubana and 1 more

This position paper argues that many persistent Large Language Model failures and emerging capabilities stem from a lack of self-consistency, proposing a unifying framework to optimize LLMs for relational properties across multiple inputs and outputs. It reinterprets diverse improvement techniques and specifies self-referential model capabilities under this self-consistency paradigm, aiming to foster more predictable and globally coherent LLMs.

Key insights

• A diverse array of persistent LLM failures can be attributed to a shared underlying problem: the lack of self-consistency in model behavior across related inputs.
• Numerous existing techniques for improving LLM behavior can be re-interpreted as special cases of optimizing a general self-consistency objective, providing conceptual unification for disparate research efforts.
• The self-consistency framework formally specifies and enables emerging 'meta-level' LLM capabilities, such as introspection, faithful chain-of-thought reasoning, calibration, and automated self-red-teaming.

Summary by alphaXiv.