aiqbench analysis
This position paper proposes "self-consistency" as a unifying framework to address persistent LLM failures like sycophancy and logical gaps by optimizing relationships across multiple inputs rather than single outputs. It argues that diverse improvement techniques are special cases of this consistency optimization, offering new properties for developing more robust language models. The work shifts the focus from isolated output evaluation to relational reasoning as a core mechanism for advancing model reliability.
Position: It's Time to Optimize LLMs for Self-Consistency
arXiv:2608.05188v1 Announce Type: new Abstract: Despite ever-increasing sophistication in language model (LM) pre- and post-training pipelines, many important failures persist: models overcondition on user framing ("sycophancy"), exhibit incomplete logical generalization, and produce confident but incorrect responses. We argue that these failures arise from a modeling assumption permeating all aspects of the pipeline: that behavior can be specified and evaluated independently on single-output pairs. Many model failures are difficult, if not impossible, to detect without reasoning about relationships between a model's responses across inputs. In this position paper, we propose self-consistency as a framework for understanding these failures. We first observe that a wide variety of techniques designed to improve specific aspects of LM behavior-targeting properties as diverse as adversarial robustness and factual coherence-can be understood as special cases of a common "consistency optimization" procedure and addressed with a standard set of optimization tools. We next outline a set of new model properties that could be achieved by optimizing for consistency, and conclude with a discussion of what it would mean to develop generally consistent LMs, including the capabilities they would enable and the objections they raise.
Abstract:Despite ever-increasing sophistication in language model (LM) pre- and post-training pipelines, many important failures persist: models overcondition on user framing ("sycophancy"), exhibit incomplete logical generalization, and produce confident but incorrect responses. We argue that these failures arise from a modeling assumption permeating all aspects of the pipeline: that behavior can be specified and evaluated independently on single-output pairs. Many model failures are difficult, if not impossible, to detect without reasoning about relationships between a model's responses across inputs. In this position paper, we propose self-consistency as a framework for understanding these failures. We first observe that a wide variety of techniques designed to improve specific aspects of LM behavior-targeting properties as diverse as adversarial robustness and factual coherence-can be understood as special cases of a common "consistency optimization" procedure and addressed with a standard set of optimization tools. We next outline a set of new model properties that could be achieved by optimizing for consistency, and conclude with a discussion of what it would mean to develop generally consistent LMs, including the capabilities they would enable and the objections they raise.
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
arXiv:2608.05188 [cs.CL]
(or arXiv:2608.05188v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2608.05188 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 277 words
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