Emergent Representations of Program Semantics in Language Models Trained on Programs (2305.11169v3)

Abstract: We present evidence that LLMs (LMs) of code can learn to represent the formal semantics of programs, despite being trained only to perform next-token prediction. Specifically, we train a Transformer model on a synthetic corpus of programs written in a domain-specific language for navigating 2D grid world environments. Each program in the corpus is preceded by a (partial) specification in the form of several input-output grid world states. Despite providing no further inductive biases, we find that a probing classifier is able to extract increasingly accurate representations of the unobserved, intermediate grid world states from the LM hidden states over the course of training, suggesting the LM acquires an emergent ability to interpret programs in the formal sense. We also develop a novel interventional baseline that enables us to disambiguate what is represented by the LM as opposed to learned by the probe. We anticipate that this technique may be generally applicable to a broad range of semantic probing experiments. In summary, this paper does not propose any new techniques for training LMs of code, but develops an experimental framework for and provides insights into the acquisition and representation of formal semantics in statistical models of code. Our code is available at https://github.com/charlesjin/emergent-semantics.

Insights into Meaning Acquisition in LLMs

The paper "Evidence of Meaning in LLMs Trained on Programs" by Charles Jin and Martin Rinard investigates whether LLMs (LMs) capture semantically meaningful representations despite being trained purely for next token prediction on syntactic text forms, specifically a corpus of computer programs. Unlike conventional natural language processing tasks, the authors leverage the structured nature of programming to precisely define concepts relevant to semantics, such as correctness and meaning, thereby using program synthesis as a rigorous framework to explore semantic acquisition in LMs.

Methodology and Core Experiments

The experimental framework centers around training a Transformer-based LM on a corpus composed of programs accompanied by program specifications in the form of input-output examples. This design allows the authors to effectively probe the LM's hidden states for semantic content during program synthesis tasks. Through a series of probing tasks, a linear classifier extracts the abstract semantic states from the model states, specifically focusing on the robot's facing direction within a grid world simulation.

The paper crucially employs an interventional procedure to dissect whether the semantics are innately learned by the LM or merely inferred by the probe from syntactic components. By manipulating the program's operational semantics while preserving its syntactic and lexical structure, the authors argue that reduced semantic extraction success from such altered semantics indicates genuine semantic acquisition by the LM rather than by the probe.

Key Findings

1. Emergence of Semantics in Training: The paper demonstrates a statistically significant correlation between the emergence of semantic content in the LM's hidden representations and the model's ability to synthesize correct programs over the course of training. This refutes the hypothesis that LMs are limited to reflecting surface-level statistical correlations.
2. Evidence of Future Semantic State Representations: The authors provide strong evidence that the LM encodes meaningful representations not only for current but also for future semantic states within programs, suggesting a predictive element in the model's representations.
3. Interventional Validation: By showing substantial degradation in semantic extraction success when intervening on semantics, the paper substantiates that the semantics are inherently captured by the LM rather than being an artifact of the probe's interpretative capability.
4. Generative and Training Discrepancies: The LM tends to produce programs that differ from the training data in semantically meaningful ways, such as generating syntactically shorter but correct programs. This finding further contests the view that LMs are restricted to learning and emulating training data distributions.

Implications and Future Directions

The results presented in this research address crucial open questions regarding the capability of LMs to internalize semantic structures purely from syntactic training. The paper's insights reinforce the notion that LMs could potentially acquire a form of semantic competence without explicit semantic signals, challenging prevalent perspectives advocating for explicit semantic grounding in language understanding models.

Practically, the implications of this work suggest a pathway towards developing more robust out-of-distribution generalization in LMs, particularly in program synthesis and related domains where semantic accuracy is paramount. Theoretically, these findings invite ongoing discourse on the nature of meaning acquisition and representation in artificial intelligence, prompting further investigation into the cognitive parallels and limitations of LMs compared to human language processing.

In future research, extending this framework to more complex semantic dimensions of programming, perhaps involving variables' data flow or more intricate logic, could provide deeper insights into LMs' semantic capabilities. Furthermore, evaluating the transferability of these semantic representations across varying domains beyond program synthesis would offer a comprehensive understanding of semantic learning in neural LLMs.