There has been considerable interest in using surprisal from Transformer-based language models (LMs) as predictors of human sentence processing difficulty. Recent work has observed an inverse scaling relationship between Transformers’ per-word estimated probability and the predictive power of their surprisal estimates on reading times, showing that LMs with more parameters and trained on more data are less predictive of human reading times. However, these studies focused on predicting latency-based measures. Tests on brain imaging data have not shown a trend in any direction when using a relatively small set of LMs, leaving open the possibility that the inverse scaling phenomenon is constrained to latency data. This study therefore conducted a more comprehensive evaluation using surprisal estimates from 17 pre-trained LMs across three different LM families on two functional magnetic resonance imaging (fMRI) datasets. Results show that the inverse scaling relationship between models’ per-word estimated probability and model fit on both datasets still obtains, resolving the inconclusive results of previous work and indicating that this trend is not specific to latency-based measures.

This paper details experiments conducted for completing the GEM 2024 Data-to-Text task for a WebNLG dataset (Gardent et al., 2017). We show that model performance varies greatly across English, Spanish, Chinese, and Russian. Data filtering was done with automatic model judgments via error detection, which performs differently per language. We report English and Spanish dev set results for a data filtering and knowledge distillation approach to generating natural language outputs for sets of triples across a variety of domains. Specifically, we compare three generation conditions: 1) few-shot prompting with ChatGPT (GPT4), 2) fine-tuning LLama2 on the unfiltered dataset, and 3) fine-tuning Llama2 on a filtered version of the dataset. Russian and Chinese efforts did not result in submissions due to inconsistent or incoherent translations being produced in either the data synthesis or final generation stages. We provide details on these shortcomings but largely focus on Spanish and English efforts that align with our task submissions. We ultimately submitted outputs in English and Spanish that were generated using a version of Llama2 fine-tuned on a filtered dataset.

We introduce a method for generating error-correction rules for grammar pattern errors in a given annotated learner corpus. In our approach, annotated edits in the learner corpus are converted into edit rules for correcting common writing errors. The method involves automatic extraction of grammar patterns, and automatic alignment of the erroneous patterns and correct patterns. At run-time, grammar patterns are extracted from the grammatically correct sentences, and correction rules are retrieved by aligning the extracted grammar patterns with the erroneous patterns. Using the proposed method, we generate 1,499 high-quality correction rules related to 232 headwords. The method can be used to assist ESL students in avoiding grammatical errors, and aid teachers in correcting students’ essays. Additionally, the method can be used in the compilation of collocation error dictionaries and the construction of grammar error correction systems.

We introduce a method for assisting English as Second Language (ESL) learners by providing translations of Collins COBUILD grammar patterns(GP) for a given word. In our approach, bilingual parallel corpus is transformed into bilingual GP pairs aimed at providing native language support for learning word usage through GPs. The method involves automatically parsing sentences to extract GPs, automatically generating translation GP pairs from bilingual sentences, and automatically extracting common bilingual GPs. At run-time, the target word is used for lookup GPs and translations, and the retrieved common GPs and their example sentences are shown to the user. We present a prototype phrase search engine, Linggle GPTrans, that implements the methods to assist ESL learners. Preliminary evaluation on a set of more than 300 GP-translation pairs shows that the methods achieve 91% accuracy.