Language Models Fail to Introspect About Their Knowledge of Language (2503.07513v2)

Abstract: There has been recent interest in whether LLMs can introspect about their own internal states. Such abilities would make LLMs more interpretable, and also validate the use of standard introspective methods in linguistics to evaluate grammatical knowledge in models (e.g., asking "Is this sentence grammatical?"). We systematically investigate emergent introspection across 21 open-source LLMs, in two domains where introspection is of theoretical interest: grammatical knowledge and word prediction. Crucially, in both domains, a model's internal linguistic knowledge can be theoretically grounded in direct measurements of string probability. We then evaluate whether models' responses to metalinguistic prompts faithfully reflect their internal knowledge. We propose a new measure of introspection: the degree to which a model's prompted responses predict its own string probabilities, beyond what would be predicted by another model with nearly identical internal knowledge. While both metalinguistic prompting and probability comparisons lead to high task accuracy, we do not find evidence that LLMs have privileged "self-access". Our findings complicate recent results suggesting that models can introspect, and add new evidence to the argument that prompted responses should not be conflated with models' linguistic generalizations.

• The paper introduces a novel framework to measure language model introspection by correlating metalinguistic responses with internal string probabilities.
• Despite high task accuracy in grammatical and word prediction domains, the study finds no significant evidence that 21 tested large language models can introspect or access their own knowledge.
• The findings suggest that introspection may not emerge naturally in current language models and might require dedicated training or architectural changes, impacting their use as linguistic intuition proxies.

Insights into Introspection Capabilities of LLMs: A Systematic Examination

This paper investigates the introspective capabilities of LLMs, focusing on their ability to discern and report their internal knowledge of language. Specifically, the paper scrutinizes LLMs across two domains—grammatical knowledge and word prediction—to determine whether these models can introspect similarly to human cognitive processes. The investigation covers 21 open-source LLMs and examines whether these models' responses to introspective prompts correspond to their string probability calculations.

Key Findings and Methodological Contributions

The authors introduce a novel framework for measuring introspection in LLMs, using the correlation between a model's metalinguistic responses and its internal string probabilities. The measure captures how well a model’s prompted introspections predict its own behavior versus those of another, similarly knowledgeable model. The paper shows that even though both metalinguistic prompting and probability comparisons yield high task accuracy, there is no substantial evidence of "self-access" in the models. This finding refines the understanding of models' introspective abilities and suggests that introspection does not spontaneously emerge as part of their knowledge representation.

Methodology and Data

The research employs both a top-down feature-based similarity approach and a bottom-up empirical similarity approach to control and assess similarities between models. This dual approach ensures a robust evaluation of models' introspective capabilities, independent of external fine-tuning or training on introspective tasks. The models were evaluated using both direct methods (through log probabilities to gauge grammaticality and word prediction) and meta methods (through metalinguistic prompts). The datasets used, such as BLiMP and custom word prediction sets, were selected to provide informative measures of these capabilities.

Analytical Techniques and Experimental Domains

The experiments are divided into two main domains:

1. Grammatical Knowledge: Here, the paper taps into minimal grammatical pairs to explore whether LLMs can align their prompted and direct evaluations of sentence grammaticality. Despite high accuracy, the paper finds low consistency between methods, suggesting a clear separation between a model’s grammatically informed outputs and its introspective capabilities.
2. Word Prediction: Through synthetic and real-world texts, this experiment tests models' abilities to predict word continuations and evaluates these against their introspective responses. Similar to the grammatical domain, higher agreement in larger models does not translate into introspective capability, as introspection does not show improvement even in simpler tasks.

Implications for Future Research and Model Development

The findings of this paper carry significant implications for both AI and linguistics research. In AI, it suggests that the current trajectory of LLM development does not naturally lead to introspective abilities akin to human metacognition, thereby implying that separate architectural or training interventions might be necessary to cultivate such capacities. For linguistics, the paper questions the validity of using LLMs as proxies for human linguistic intuitions, especially in deriving grammatical knowledge through introspective prompts.

This research further suggests that introspection in LLMs should not be presumed based on their linguistic outputs or performance on compelling linguistic tasks. Instead, detailed measures, such as those introduced in this paper, should be used to understand and validate any claims about introspective capabilities.

Future Directions

While the paper does not find evidence of introspection within the evaluated LLMs, it opens several pathways for future work. There is scope for exploring architectural changes or novel training schemes that specifically aim to endow models with introspective capabilities. Additionally, the community might investigate other domains of cognitive capabilities beyond current linguistic tasks to assess where and how LLMs might naturally acquire introspective features.

Overall, this paper advances our understanding of LLMs' internal processing mechanisms and provides a foundational framework upon which future introspective analyses could be built, fostering deeper insights into the nuanced capabilities of artificial linguistic systems.