Papers by Maggie Mi
Enhancing Linguistic Competence of Language Models through Pre-training with Language Learning Tasks (2026.acl-short)
Copied to clipboard
| Challenge: | Language models (LMs) are pre-trained on raw text datasets to generate text sequences token-by-token. |
| Approach: | They propose a framework that integrates Language Learning Tasks alongside standard next-token prediction to stimulate the acquisition of morphological, syntactic, and semantic knowledge. |
| Outcome: | The proposed framework improves performance on linguistic competence benchmarks while maintaining competitive performance on reasoning tasks. |
Rethinking the Idiomaticity Decomposability Hypothesis: Evidence from Distributional Learning (2026.acl-long)
Copied to clipboard
| Challenge: | Decomposability is thought to predict syntactic flexibility, but is not attributed to distributional experience. |
| Approach: | They propose a model-internal measure of decomposability and relate it to human ratings, syntactic flexibility, and predictability while tracking idiom learning during pretraining. |
| Outcome: | The proposed model-internal measure correlates weakly with human judgments and shows a small but consistent negative relationship with syntactic flexibility. |
Rolling the DICE on Idiomaticity: How LLMs Fail to Grasp Context (2025.acl-long)
Copied to clipboard
| Challenge: | Existing models fail to resolve idiomaticity when it depends on contextual understanding . idiom frequency influences performance but does not guarantee accurate interpretation. |
| Approach: | They propose a novel contrastive dataset to assess whether large language models can effectively leverage context to disambiguate idiomatic meanings. |
| Outcome: | The proposed model performs better on sentences deemed more likely by the model . collocational frequency and sentence probability influence performance but not accuracy . |
From Input Perception to Predictive Insight: Modeling Model Blind Spots Before They Become Errors (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for error or uncertainty estimation rely on logits, entropy, sampling variance. |
| Approach: | They propose an input-only method for anticipating language model errors using token-level likelihood features inspired by surprisal and the Uniform Information Density hypothesis. |
| Outcome: | The proposed method outperforms baseline models and standard models on linguistic datasets. |