Does Liking Yellow Imply Driving a School Bus? Semantic Leakage in Language Models (2408.06518v3)

Abstract: Despite their wide adoption, the biases and unintended behaviors of LLMs remain poorly understood. In this paper, we identify and characterize a phenomenon never discussed before, which we call semantic leakage, where models leak irrelevant information from the prompt into the generation in unexpected ways. We propose an evaluation setting to detect semantic leakage both by humans and automatically, curate a diverse test suite for diagnosing this behavior, and measure significant semantic leakage in 13 flagship models. We also show that models exhibit semantic leakage in languages besides English and across different settings and generation scenarios. This discovery highlights yet another type of bias in LLMs that affects their generation patterns and behavior.

• The paper introduces semantic leakage as the unintended influence of prompt semantics on language model outputs.
• The authors apply both human and automated evaluations across 13 models to measure semantic leakage systematically.
• The study reveals that instruction-tuned models show higher leakage, urging a rethinking of bias-mitigation strategies.

Semantic Leakage in LLMs: An Analytical Overview

This paper explores the newly identified phenomenon of semantic leakage in LMs, focusing on how these models inadvertently integrate irrelevant contextual information from prompts during text generation. Despite the widespread deployment of LMs across various applications, the intricacies of their biases and unintended behaviors require further exploration. The paper introduces semantic leakage as a type of bias characterized by unintended semantic associations between prompt elements and model output, manifesting in generation patterns that are stronger than expected based on natural language distributions.

Characterization and Detection of Semantic Leakage

The authors present an evaluation framework to systematically diagnose and measure semantic leakage, utilizing both human and automated methods. They define semantic leakage as the undue influence of a prompt's semantic features on the resulting generation. This metric is operationalized by measuring the semantic similarity between the generated text and a specific concept from the prompt, compared to a control prompt free from extraneous semantic cues.

The research team curated a diverse test suite encompassing a broad set of categories, such as colors, animals, and occupations, to examine leakage across 13 prominent LLMs. This setup aids in isolating the semantic association introduced by the prompt from the expected natural language response, illuminating the extent to which these models exhibit semantic leakage.

Key Findings

Significant semantic leakage was observed across all models tested, including notable instances beyond the English language and in various generation contexts. Notably, models that have undergone instruction tuning, such as GPT-finetuned versions, displayed higher leakage rates. These findings were validated through both automated metrics and human evaluations, suggesting a pervasive underlying bias in LMs stemming from their predisposed learning of associations during training.

The paper highlights the implication of such leakage, particularly in relation to previously documented model biases such as gender and racial biases. Semantic leakage serves as a broader indicator of how extensive learned associations might influence model behaviors and outputs, potentially complicating efforts for bias mitigation.

Implications and Future Directions

The identification of semantic leakage has significant implications for the future design and deployment of LLMs. Understanding this unwanted influence could enhance the refinement of generative behaviors to align them more closely with human expectations for coherence and relevance. Furthermore, this phenomenon suggests a novel dimension of cognitive-like bias within LMs—semantic priming—that parallels human psychological processes.

Moving forward, the exploration of the mechanisms underlying semantic leakage, especially within instruction-tuned models, could unveil more general association tendencies that current mitigation strategies do not fully address. Future research might focus on determining the precise cognitive or methodological roots contributing to this leakage, evaluating its impacts in real-world applications, and adjusting training regimes to counteract this bias effectively. Expanding the analysis to include a more extensive array of languages and model types could further illuminate the scope and variations of this effect within different linguistic and cultural contexts.

In conclusion, the paper of semantic leakage presents a fertile area for further investigation, potentially bridging gaps in understanding the nuanced behaviors of LMs and steering advancements in developing more robust, bias-aware models.