How Large Language Models Get Stuck: Early structure with persistent errors

2026-02-27

AI Summary

This study investigates how large language models are susceptible to bigram statistical biases during early training, leading to entrenched preferences for incorrect syntactic structures and persistent failure on approximately one-third of BLiMP grammatical categories. By training OPT models on the BabyLM dataset and integrating BLiMP benchmark evaluation, linguistic rule analysis, and training dynamics tracking, the authors propose the “bigram grammar hypothesis”: erroneous classifications driven by early bigram co-occurrence biases become固化ated as persistent inductive biases that impede subsequent syntactic learning. The work not only reveals fundamental limitations in models’ grammatical acquisition but also provides preliminary validation of certain BLiMP test items and establishes a reproducible analytical framework with empirical pathways for testing this hypothesis.

Technology Category

• Natural Language Processing: (Large) Language Models
• Machine Learning: Large Multimodal Models (LMMs)
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Linguistic insights may help make Large Language Model (LLM) training more efficient. We trained Meta's OPT model on the 100M word BabyLM dataset, and evaluated it on the BLiMP benchmark, which consists of 67 classes, each defined by sentence pairs that differ in a targeted syntactic or semantic rule violation. We tested the model's preference for grammatical over ungrammatical sentences across training iterations and grammatical types. In nearly one-third of the BLiMP classes, OPT fails to consistently assign a higher likelihood to grammatical sentences, even after extensive training. When it fails, it often establishes a clear (erroneous) separation of the likelihoods at an early stage of processing and sustains this to the end of our training phase. We hypothesize that this mis-categorization is costly because it creates entrenched biases that must, eventually, be reversed in order for the model to perform well. We probe this phenomenon using a mixture of qualitative (based on linguistic theory and the theory of Deep Learning) and quantitative (based on numerical testing) assessments. Our qualitative assessments indicate that only some BLiMP tests are meaningful guides. We conclude by articulating a hypothesis, the Bigram Hypothesis, which claims that the learning process will exhibit erroneous entrenchment if bigram statistics bias the model toward wrong distinctions early in training, and we describe a method (in progress) of testing the hypothesis on appropriately selected BLiMP classes.

Problem

• Large Language Models
• grammatical errors
• training dynamics
• persistent biases
• BLiMP benchmark

Innovation

• Large Language Models
• training dynamics
• grammatical bias
• Bigram Hypothesis
• BLiMP benchmark