Predicting Emergent Capabilities by Finetuning (2411.16035v1)

Abstract: A fundamental open challenge in modern LLM scaling is the lack of understanding around emergent capabilities. In particular, LLM pretraining loss is known to be highly predictable as a function of compute. However, downstream capabilities are far less predictable -- sometimes even exhibiting emergent jumps -- which makes it challenging to anticipate the capabilities of future models. In this work, we first pose the task of emergence prediction: given access to current LLMs that have random few-shot accuracy on a task, can we predict whether future models (GPT-N+1) will have non-trivial accuracy on that task? We then discover a simple insight for this problem: finetuning LLMs on a given task can shift the point in scaling at which emergence occurs towards less capable models. To operationalize this insight, we can finetune LLMs with varying amounts of data and fit a parametric function that predicts when emergence will occur (i.e., "emergence laws"). We validate this approach using four standard NLP benchmarks where large-scale open-source LLMs already demonstrate emergence (MMLU, GSM8K, CommonsenseQA, and CoLA). Using only small-scale LLMs, we find that, in some cases, we can accurately predict whether models trained with up to 4x more compute have emerged. Finally, we present a case study of two realistic uses for emergence prediction.

• The paper introduces a finetuning method that predicts emergent capabilities in LLMs based on pre-emergence model behavior.
• It employs task-specific finetuning across NLP benchmarks (e.g., MMLU, GSM8K) to forecast capability leaps with quantifiable accuracy.
• The work bridges scalable pretraining laws with emergent abilities, offering actionable insights for cost-efficient model development and AI safety.

Predicting Emergent Capabilities by Finetuning

The paper under review presents a methodological contribution to the longstanding challenge of predicting emergent capabilities in LLMs. These emergent capabilities refer to sudden qualitative jumps in functionality observed when training LLMs surpasses certain scale thresholds. Understanding and predicting these phenomena is crucial for both developers and policymakers who need to anticipate the features and risks of future LLM iterations.

Overview and Methodology

The paper’s central question is whether we can predict emergent capabilities in future model iterations (specifically from GPT-N to GPT-N+1), using characteristics observed from current, less capable models (termed "pre-emergence" models). The authors propose that task-specific finetuning on existing LLMs not only enhances their abilities but also shifts the scale threshold at which emergence occurs. This insight forms the basis of their approach, which includes developing a predictive "emergence law".

To construct this emergence law, the authors finetune models on specific tasks using varying amounts of data, subsequently fitting these data points to a parametric function to predict the emergence point under few-shot learning conditions. The empirical validation was conducted using four prominent NLP benchmarks: MMLU, GSM8K, CommonsenseQA, and CoLA. Their results suggest that even small-scale models can provide signals that predict whether significantly larger models, trained with up to 4x more computational resources, will demonstrate emergent capabilities.

Experimental Results and Validation

The results are compelling. On the four benchmarks, models finetuned with this approach could reliably indicate when larger models will demonstrate non-trivial accuracy improvements. For instance, finetuning allowed for accurate predictions about the emergent capabilities of models trained with an extensive compute budget, validating the notion that even small models bear potential insight about larger ones when augmented by finetuning.

Additionally, the authors present two case studies demonstrating practical applications: using emergence prediction for assessing pretraining data quality and predicting more complex capabilities akin to those in advanced AI systems. Notably, their method can infer data quality by considering the disparities in the emergence points of two distinct LLMs trained on different corpora.

Theoretical and Practical Implications

Theoretically, the approach bridges the gap between scalable pretraining laws and less predictable downstream emergent capabilities. It suggests a quantitative methodology to explore emergent phenomena and provides a framework for extending scaling laws into the field of qualitative capability improvement.

Practically, the implications span several domains: AI developers can leverage these insights for making architectural or data-driven decisions cost-effectively, without needing to scale models vertically at prohibitive costs initially. From a policy standpoint, organizations can prepare more effectively for future advancements by predicting emergence in advance, thus contributing to AI safety and planning.

Future Directions

While the methodology holds significant promise, further exploration is warranted into understanding the underlying mechanics of finetuning-induced shifts in emergence thresholds. Additionally, there is potential for refining data collection strategies to enhance the prediction accuracy further or extending the approach to other neural architectures beyond transformers. Exploring these avenues could yield models capable of predicting emergence under a broader spectrum of conditions and tasks, thus enriching our understanding of deep learning models' development trajectory.

In summary, this work makes an important step in demystifying the emergent capabilities in LLMs, offering a novel perspective on using present-day models to forecast the capabilities of future iterations effectively.