Fifty shapes of BLiMP: syntactic learning curves in language models are not uniform, but sometimes unruly

Authors: bastian  bunzeck
sina  zarrieß
Venue: EMNLP_2024_Workshop_BlackBoxNLP
Published: 2024-08-01 09:54:37

Abstract: Syntactic learning curves in LMs are usually re- ported as stable and power law-shaped. By an- alyzing the learning curves of different LMs on various syntactic phenomena using small, self- trained llama models and larger, pre-trained pythia models, we show that while many phe- nomena do follow typical power law curves, others exhibit S-shaped, U-shaped, or erratic patterns. Certain syntactic paradigms remain challenging even for large models. Moreover, most phenomena show similar curves for their concrete paradigms, but the existence of di- verging patterns and oscillations indicates that average curves mask important developmental differences.

Main paper: 
 learning curves: 
 Existing empirical evidence seems to suggest that morphological, syntactic and basic semantic knowledge in language models is acquired quite early during pre-training, normally with a power-law like increase over the first 5-15% of the first training epoch (inter alia Chiang et al., 2020; Liu et al., 2021; Saphra, 2021; Müller-Eberstein et al., 2023) . However, evaluation protocols that assess concrete learning trajectories of LMs are only beginning to emerge. Current probing approaches often mask developmental difficulties by reporting averaged scores over large and varied evaluation data sets, although, as Ritter and Schooler (2001) note, "[a] veraging can mask important aspects of learning". In reality, not every learning curve is monotonically increasing. Exceptions include phase transitions with sudden performance boosts (Viering and Loog, 2023) , peaks (Nakkiran, 2019) , dips (Loog and Duin, 2012) , and curves that oscillate through several maxima and plateaus (Sollich, 2001) . 
methods: 
 Models We analyze two different model architectures, four llama models (Touvron et al., 2023) trained on the 10M and 100M BabyLM 2023 data sets (Warstadt et al., 2023) , and six pythia models (Biderman et al., 2023) trained on the much larger The Pile data set (Gao et al., 2020) .Evaluation We test linguistic knowledge as BLiMP performance with lm-eval-harness (Gao et al., 2022) . BLiMP can be used to discern whether a grammatical sentence is preferred by an LM ( lower perplexity): an accuracy of 50% equals the random baseline. We evaluate across the first training epoch and look at logarithmically spaced evaluation checkpoints: 10 checkpoints within the first 10% of training and 9 additional checkpoints until the epoch's completion.Curves We devise our own classification scheme for learning curves based on the distinction between well-and ill-behaved curves in Viering and Loog (2023) , and refine the categories with patterns attested in language acquisition (like U-shaped learning, see Saxton, 2009) . Table 1 gives a brief overview. We qualitatively assign shapes to the learning curves, aided by fitting fifth-degree polynomials to each curve. 
results: 
 The learning curves for all BLiMP phenomena and models are visualized in Figure 1 . From our qualitative and quantitative analyses, the most striking observations can be summarized as follows:• Ill-behaved curves occur across all models, though they are less frequent in larger models with more internal parameters. When looking at non-averaged curves, these ill-behaved developments are much more pronounced. • For many phenomenon-model combinations, the curves for related paradigms emerge as similarly shaped sheaves of individual curves. This is particularly true for, e.g., argument structure or determiner-noun agreement.• In contrast to these sheaves, also diverging patterns are observed within phenomena. Some paradigms within the same phenomenon have mirrored learning trajectories, where improvement in one paradigm correlates with diminishing performance in another. This divergence is particularly pronounced for filler-gap phenomena, as well as in subject-verb agreement and binding.• Shape-wise similarities are more pronounced for phenomena across different models, whereas (especially for the smaller models) there is high variation within models.We conclude that while the rapid syntax learning assumption from earlier studies generally holds, it also needs revision. When averaging across many phenomena, performance gains seem to follow a prototypical power law. This is not true when examining individual phenomena, many of which exhibit ill-behaved curves. Stability in BLiMP performance is often an illusion; stable average curves are based on oscillating minimal pair paradigms within them. With larger models and more data, there is a general shift towards greater stability and more power law curves, but even in very large models, not everything is learned optimally.Published at Multimodality and Interaction in Language Learning (MILLing), see https://aclanthology. org/venues/clasp/ 
: 
 Syntactic learning curves in LMs are usually reported as stable and power law-shaped. By analyzing the learning curves of different LMs on various syntactic phenomena using small, selftrained llama models and larger, pre-trained pythia models, we show that while many phenomena do follow typical power law curves, others exhibit S-shaped, U-shaped, or erratic patterns. Certain syntactic paradigms remain challenging even for large models. Moreover, most phenomena show similar curves for their concrete paradigms, but the existence of diverging patterns and oscillations indicates that average curves mask important developmental differences.1  
 
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