2024 AreEmergentAbilitiesofLargeLang

• (Schaeffer et al., 2024) ⇒ Rylan Schaeffer, Brando Miranda, and Sanmi Koyejo. (2024). “Are Emergent Abilities of Large Language Models a Mirage?.” In: Advances in Neural Information Processing Systems, 36.

Subject Headings: LLM Scaling Laws.

Notes

1. Emergent Abilities in Large Language Models: The paper serves as a key reference for understanding and questioning the notion of emergent abilities that purportedly arise in large language models as they scale in parameter count.
2. Role of Evaluation Metrics: It provides in-depth discussion and examples demonstrating how non-linear and discontinuous metrics (e.g., exact string match) can artificially create or exaggerate emergent behaviors.
3. Smooth vs. Abrupt Performance Improvements: The authors present mathematical models showing how seemingly abrupt changes in performance can be explained by continuous improvements in per-token accuracy, distorted by the chosen metric.
4. Arithmetic Tasks as a Case Study: The paper offers detailed experimentation on multi-digit arithmetic (addition, multiplication) with GPT-3, illustrating how metrics like exact match can produce the appearance of sudden leaps.
5. Comparison of Linear and Non-Linear Metrics: It contrasts linear metrics (e.g., edit distance) with non-linear or discontinuous metrics (e.g., exact match), highlighting how the choice can yield very different performance curves.
6. Analysis of BIG-Bench Emergence Claims: Through a meta-analysis of BIG-Bench tasks, the paper evaluates which metrics are most prone to showing “[emergent]” behavior, shedding light on how these phenomenon often concentrate in a small subset of tasks/metrics.
7. Induced Emergence in Vision Models: By replicating the same phenomenon (apparent emergence) in vision tasks (e.g., MNIST, CIFAR100) using specific metrics, the paper underscores that emergent effects are not exclusive to language models.
8. Statistical Resolution and Sample Size: The authors emphasize the importance of test-set size for accurately gauging small but continuous improvements, debunking zero-to-one leaps that may just be artifacts of insufficient resolution.
9. Scaling Laws Revisited: This work situates its findings within the broader context of neural scaling laws, reinforcing the idea that smooth performance trends can appear abrupt if measured incorrectly.
10. Benchmark Design and Interpretation: It provides guidance on how benchmark creators and researchers can better design tasks and choose metrics that accurately reflect continuous improvement instead of confounding real capabilities with artificial thresholding.

Cited By

• http://scholar.google.com/scholar?q=%222024%22+Are+Emergent+Abilities+of+Large+Language+Models+a+Mirage%3F

2021

• (Jones, 2021) ⇒ Andy L. Jones. (2021). “Scaling Scaling Laws with Board Games.” doi:10.48550/arXiv.2104.03113
  • QUOTE: “One reason to believe this is the phenomenon known as neural scaling laws: empirical observations that deep networks exhibit power law scaling in the test loss as a function of training dataset size, number of parameters or compute [15, 32, 13, 18, 3, 10, 14, 17, 39, 16, 8, 29].”

Quotes

Abstract

Recent work claims that large language models display emergent abilities, abilities not present in smaller-scale models but observed in larger-scale models. What makes emergent abilities intriguing is two-fold: their sharpness, transitioning seemingly instantaneously from not present to present, and their unpredictability, appearing at seemingly unforeseeable model scales. Here, we present an alternative explanation for emergent abilities: that for a particular task and model family, when analyzing fixed model outputs, emergent abilities appear due to the researcher’s choice of metric rather than due to fundamental changes in model behavior with scale. Specifically, nonlinear or discontinuous metrics produce apparent emergent abilities, whereas linear or continuous metrics produce smooth, continuous, predictable changes in model performance. We present our alternative explanation in a simple mathematical model, then test it in three complementary ways: we (1) make, test, and confirm three predictions on the effect of metric choice using the InstructGPT / GPT-3 family on tasks with claimed emergent abilities, (2) make, test, and confirm two predictions about metric choices in a meta-analysis of emergent abilities on BIG-Bench, and (3) show how to choose metrics to produce never-before-seen seemingly emergent abilities in multiple vision tasks across diverse deep networks. Via all three analyses, we provide evidence that alleged emergent abilities evaporate with different metrics or with better statistics, and may not be a fundamental property of scaling AI models.

1. Introduction

• NOTE: Highlights the recent excitement around emergent abilities in large language models (LLMs), describing how these seemingly sudden capabilities have raised questions about model unpredictability, safety, and alignment. The authors present their central argument: that many of these abrupt changes are driven by the researcher’s choice of evaluation metric rather than by fundamental shifts in model behavior.

2. Alternative Explanation for Emergent Abilities

• NOTE: Proposes that emergent abilities arise from the use of nonlinear or discontinuous evaluation metrics rather than from genuine jumps in underlying model performance. The authors introduce a simple mathematical model to demonstrate how continuous improvements in error rates can appear as sharp transitions when measured with metrics like exact string match or multiple-choice grading.

3. Analyzing InstructGPT/GPT-3’s Emergent Arithmetic Abilities

• NOTE: Investigates the arithmetic abilities of GPT-3 and InstructGPT on tasks such as multi-digit addition and multiplication. The authors show that replacing exact-match accuracy (a nonlinear metric) with token-level edit distance (a linear metric) transforms abrupt, emergent gains into smooth, predictable improvements. Increasing the test set size further reveals that smaller models perform above zero, undermining the appearance of a sudden leap.

4. Meta-Analysis of Claimed Emergent Abilities

• NOTE: Conducts a meta-analysis of BIG-Bench tasks, a benchmark often cited for emergent behaviors. Finds that most emergent ability claims are tied to a small set of nonlinear metrics, such as exact string match and multiple-choice accuracy. Switching to more continuous metrics like the Brier Score eliminates these apparent leaps, supporting the argument that metric choice drives perceived emergence.

5. Inducing Emergent Abilities in Networks on Vision Tasks

• NOTE: Extends the analysis to vision tasks, using deep networks such as autoencoders and transformers. By employing discontinuous scoring schemes (e.g., threshold-based reconstruction metrics), the authors replicate emergent behaviors in image classification and reconstruction tasks, despite underlying performance improving gradually.

6. Limitations

• NOTE: Acknowledges that the authors are not dismissing all emergent behaviors as artifacts or claiming that larger models never develop new capabilities. They note constraints such as limited access to private model families and the potential existence of genuinely emergent phenomena. Additionally, some real-world metrics may inherently be discontinuous, complicating the identification of “[true]” emergent abilities.

7. Related Work

• NOTE: Situates the paper within the context of emergent properties research, neural scaling laws, and discussions about abrupt skill acquisition. Highlights alternative explanations, such as piecewise power-law fits or discrete phenomena in language data, suggesting that these can coexist with the metric-driven explanation.

8. Discussion

• NOTE: Reflects on the implications of metric design for interpreting model performance. Argues for the development of more nuanced benchmarks that separate the task itself from the metric used to evaluate it. Emphasizes the benefits of linear or continuous metrics for providing a clearer signal of model improvement, while acknowledging that some real-world applications may involve unavoidable threshold effects.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2024 AreEmergentAbilitiesofLargeLang	Rylan Schaeffer Brando Miranda Sanmi Koyejo			Are Emergent Abilities of Large Language Models a Mirage?						2024