oLMpics -- On what Language Model Pre-training Captures (1912.13283v2)

Abstract: Recent success of pre-trained LLMs (LMs) has spurred widespread interest in the language capabilities that they possess. However, efforts to understand whether LM representations are useful for symbolic reasoning tasks have been limited and scattered. In this work, we propose eight reasoning tasks, which conceptually require operations such as comparison, conjunction, and composition. A fundamental challenge is to understand whether the performance of a LM on a task should be attributed to the pre-trained representations or to the process of fine-tuning on the task data. To address this, we propose an evaluation protocol that includes both zero-shot evaluation (no fine-tuning), as well as comparing the learning curve of a fine-tuned LM to the learning curve of multiple controls, which paints a rich picture of the LM capabilities. Our main findings are that: (a) different LMs exhibit qualitatively different reasoning abilities, e.g., RoBERTa succeeds in reasoning tasks where BERT fails completely; (b) LMs do not reason in an abstract manner and are context-dependent, e.g., while RoBERTa can compare ages, it can do so only when the ages are in the typical range of human ages; (c) On half of our reasoning tasks all models fail completely. Our findings and infrastructure can help future work on designing new datasets, models and objective functions for pre-training.

Analyzing the Reasoning Capabilities of LLMs with oLMpics

The paper, "oLMpics - On what LLM Pre-training Captures," contributes to the ongoing exploration of pre-trained LLM (LM) capabilities, especially focusing on symbolic reasoning tasks. LLMs have achieved notable success in natural language processing, albeit with insufficient understanding of their reasoning capabilities. This paper proposes eight tasks to assess these abilities, offering insights into whether performance stems from pre-training or fine-tuning processes.

Key Findings and Methodology

The core contribution of this paper is the introduction of eight reasoning tasks examining operations such as comparison, conjunction, and composition. Additionally, it proposes a dual evaluation protocol combining zero-shot evaluation and learning curves. This approach distinguishes knowledge embedded via pre-training from that acquired during fine-tuning, showcasing the inherent or latent capabilities of different LMs.

1. Divergent Reasoning Abilities: The findings reveal that models such as BERT, RoBERTa, and others exhibit distinct reasoning competencies. For instance, RoBERTa excelled in reasoning tasks that rendered BERT ineffective, indicating qualitative differences despite structural similarities.
2. Context Dependency and Limitations: The paper uncovers context dependency in LMs—highlighting that these models may perform well in typical data representations but falter outside these scenarios. For instance, models struggled with age comparisons beyond expected human age ranges, suggesting a lack of true abstraction or generalization capability.
3. Performance Discrepancies: Across half the reasoning tasks, all models failed completely. This underscores existing gaps in LM capabilities—particularly concerning tasks involving uncommon or nuanced reasoning.
4. Model Analysis: The methodology utilizes masked LLMing (MLM) and multi-choice question answering (MC-QA) for probing. Choices such as MC-MLM enable the paper of pre-trained representations sans fine-tuning, while learning curves assess the quick adaptation of models to these tasks with minimal additional data.

Practical and Theoretical Implications

The implications of this paper transcend the immediate understanding of model capabilities.

• Dataset and Model Design: These findings can guide the future design of datasets and pre-training objective functions to address known limitations in LM reasoning capabilities. Specifically, tasks can be crafted to encompass symbolic reasoning, which LMs struggle with.
• Improvement of Pre-training Strategies: The results suggest potential modifications to pre-training methodologies to embed more robust contextual reasoning. For instance, changes in training corpora composition or objectives could be explored to improve performance in abstract reasoning tasks.
• AI Model Evaluation: The established benchmarks provide a reference framework to evaluate current and future LMs, promoting a deeper understanding of models' reasoning abilities and limitations.

Future Developments

Future research will likely expand on the methodologies proposed—expanding tasks, evaluating new model architectures or learning paradigms, and continuously refining the distinction between knowledge gained through pre-training versus that obtained through fine-tuning. The ultimate aim remains to achieve models that understand and apply complex reasoning abstractively, independent of specific contexts.

This paper lays the groundwork for systematically examining reasoning in LMs, encouraging the development of models that are not only statistically proficient but also adept at abstract, symbolic thought processes.