Code Pretraining Improves Entity Tracking Abilities of Language Models (2405.21068v1)

Abstract: Recent work has provided indirect evidence that pretraining LLMs on code improves the ability of models to track state changes of discourse entities expressed in natural language. In this work, we systematically test this claim by comparing pairs of LLMs on their entity tracking performance. Critically, the pairs consist of base models and models trained on top of these base models with additional code data. We extend this analysis to additionally examine the effect of math training, another highly structured data type, and alignment tuning, an important step for enhancing the usability of models. We find clear evidence that models additionally trained on large amounts of code outperform the base models. On the other hand, we find no consistent benefit of additional math training or alignment tuning across various model families.

• The paper demonstrates that pretraining on code data notably improves language models' entity tracking across various sizes and model families.
• The study employs a unique 'boxes task' to measure state changes, quantifying tracking accuracy against a robust random baseline.
• Findings indicate that structured, procedural datasets like code offer a stronger training signal for entity tracking than math training or alignment tuning.

Code Pretraining Improves Entity Tracking Abilities of LLMs

The paper "Code Pretraining Improves Entity Tracking Abilities of LLMs" investigates the impact of code pretraining on the entity tracking capabilities of LLMs. The authors, Najoung Kim, Sebastian Schuster, and Shubham Toshniwal, conduct a series of experiments to validate the hypothesis that training LLMs on structured data, such as code, enhances their ability to track discourse entities within natural language texts.

Key Findings

1. Entity Tracking in LLMs: The primary focus of this paper is the entity tracking ability of LLMs, an essential component for understanding long-context narratives and operations such as planning. The authors explore this capability through a specialized task involving dynamic state changes in a text-based scenario.
2. Experimental Design: The authors design a series of experiments comparing base LLMs with versions of those models trained on additional code data. They also investigate the effects of math training and alignment tuning to assess whether other forms of structured data impact entity tracking.
3. Structured Data Impact: The results clearly indicate that code pretraining significantly enhances the entity tracking abilities of LLMs, outclassing the performance of base models across various sizes and model families. This suggests that the structured procedural aspects of code provide a robust training signal related to state tracking.
4. Math Training and Alignment Tuning: Unlike code, additional math training does not consistently enhance the model's ability to track entities. Similarly, alignment tuning benefits base models more than those pretrained on code, illustrating the nuanced effects of different training regimens.
5. Implications for LLMs: The findings support the view that pretraining on large volumes of code not only benefits tasks like code generation but also improves the general reasoning and contextual tracking abilities of LLMs.

Methodological Details

• Model Selection:

The paper involves a range of open-source LLMs where the pretraining processes have been documented. This transparency allows for reliable comparisons between minimally altered model pairs, ensuring the validity of the reported effects of code and math pretraining.

• Evaluation Setup:

A "boxes task" is used to evaluate entity tracking, where LLMs report the contents of boxes after a series of operations manipulate those contents. This structured task is particularly well-suited to gauging state-tracking efficacy.

• Quantitative Metrics:

The models' performances are reported against a strong random baseline, and accuracy is detailed according to the number of operations affecting entity states, highlighting the models' capacity to handle dynamic changes.

Implications and Future Directions

The empirical evidence from this paper underscores the utility of including structured data like code in pretraining datasets for strengthening entity tracking and reasoning tasks. It implies that integrating structured procedural knowledge may be vital for developing more sophisticated and capable AI systems, enhancing their application in complex real-world scenarios.

For future research, the paper opens avenues to explore precisely how structured data influences learning mechanisms within models, potentially leading to the deliberate design of datasets that cater to specific cognitive tasks such as entity tracking. Further investigations may also address the confounding effects identified, such as the scale of training data and variations in fine-tuning methods, to refine and expand upon these findings.

Overall, this research contributes significant insights into the nuanced effects of dataset composition on model capabilities, suggesting a path toward more efficient and effective LLM training strategies.