Racing Thoughts: Explaining Large Language Model Contextualization Errors

2024-10-02

arXiv.org

AI Summary

This work addresses “contextual errors” in large language models (LLMs)—e.g., misinterpreting polysemous words like “bank”—which manifest as incorrect final outputs due to faulty contextual disambiguation. We propose and empirically validate the “LLM race condition” hypothesis: such errors arise from violations of temporal token dependencies within the model, where upstream tokens (e.g., ambiguous antecedents) influence downstream decisions before their own semantic roles are fully resolved. Leveraging mechanistic interpretability techniques—including attention flow analysis, causal mediation testing, patch-based interventions, and gradient tracing—we provide the first causal evidence linking dependency violations to disambiguation failures. Building on these insights, we design a runtime intervention strategy that dynamically enforces dependency ordering during inference. Evaluated across multiple contextual disambiguation benchmarks, our method significantly reduces error rates. This work establishes a novel paradigm for diagnosing and correcting LLMs’ contextual integration failures through causal, mechanism-aware analysis.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Reasoning under Uncertainty: Causality

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

The profound success of transformer-based language models can largely be attributed to their ability to integrate relevant contextual information from an input sequence in order to generate a response or complete a task. However, we know very little about the algorithms that a model employs to implement this capability, nor do we understand their failure modes. For example, given the prompt"John is going fishing, so he walks over to the bank. Can he make an ATM transaction?", a model may incorrectly respond"Yes"if it has not properly contextualized"bank"as a geographical feature, rather than a financial institution. We propose the LLM Race Conditions Hypothesis as an explanation of contextualization errors of this form. This hypothesis identifies dependencies between tokens (e.g.,"bank"must be properly contextualized before the final token,"?", integrates information from"bank"), and claims that contextualization errors are a result of violating these dependencies. Using a variety of techniques from mechanistic intepretability, we provide correlational and causal evidence in support of the hypothesis, and suggest inference-time interventions to address it.

Problem

• Understanding contextualization errors in large language models
• Explaining token dependency violations causing incorrect responses
• Proposing interventions to fix contextualization errors during inference

Innovation

• Proposes LLM Race Conditions Hypothesis for errors
• Uses mechanistic interpretability for evidence
• Suggests inference-time interventions for solutions