| Challenge: | Syntactic control of generated text is crucial for many domain-specific applications of language models, where structural constraints, such as formality, grammatical correctness, or adherence to a given template, can significantly affect usability and readability. |
| Approach: | They propose to use posterior inference to enforce target constituency structure by sampling from a proposal distribution with a syntactic tagger to ensure that each generated token aligns with the desired syntakic structure. |
| Outcome: | The proposed method increases the F1 score from 12.31 (GPT2-large) and 35.33 (Llama3-8B) to about 93 in both cases without compromising the language model’s fluency. |
Similar Papers
Explicit Syntactic Guidance for Neural Text Generation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing text generation models follow the sequence-to-sequence paradigm . generative grammar suggests humans generate language by learning language grammar . |
| Approach: | They propose a syntax-guided generation schema that searches the syntax tree in a top-down direction. |
| Outcome: | The proposed method outperforms autoregressive baselines on paraphrase generation and machine translation. |
Refining Targeted Syntactic Evaluation of Language Models (2021.naacl-main)
Copied to clipboard
| Challenge: | Targeted syntactic evaluation of subject-verb number agreement in English (TSE) evaluates language models’ syntaktic knowledge using hand-crafted minimal pairs of sentences that differ only in the main verb’s conjugation. |
| Approach: | They propose to use templates to evaluate language models' syntactic knowledge to assess their ability to conjugate arbitrary verbs and their likely behavior to measure their likelihood of conjugating grammatical sentences. |
| Outcome: | The proposed evaluations overestimate systematicity of language models, but score up to 40% better on verbs that they predict are likely in context. |
Step-by-Step: Controlling Arbitrary Style in Text with Large Language Models (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing methods for autoregressive text generation have low controllability and accumulating errors. |
| Approach: | They propose a three-stage prompt-based approach to express autoregressive text in a specific region editing task using a word frequency-based strategy. |
| Outcome: | Experiments on publicly competitive datasets confirm that the proposed approach achieves state-of-the-art performance. |
Exploiting Syntactic Structure for Better Language Modeling: A Syntactic Distance Approach (2020.acl-main)
Copied to clipboard
| Challenge: | incorporating syntactic structure into language models has been a challenge since the 1990s. |
| Approach: | They propose to use syntactic information to integrate syntastic structure into neural language models by providing ground truth parse trees as additional training signals. |
| Outcome: | The proposed model achieves lower perplexity and better quality when ground truth parse trees are provided as training signals. |
Targeted Syntactic Evaluation of Language Models (D18-1)
Copied to clipboard
| Challenge: | Recent advances have led to an explosion of neural network-based LM architectures. |
| Approach: | They propose to supplement perplexity with a metric that assesses whether a language model can predict the grammatical sentence more accurately than an ungrammatically-based model. |
| Outcome: | The proposed model performed poorly on many of the constructions. |
Leveraging pre-trained language models for linguistic analysis: A case of argument structure constructions (2024.emnlp-main)
Copied to clipboard
| Challenge: | Argument structure constructions (ASCs) are lexicogrammatical patterns at the clausal level. |
| Approach: | They evaluate the effectiveness of pre-trained language models in identifying argument structure constructions . they use supervised training with RoBERTa and prompt-guided annotation with GPT-4 . |
| Outcome: | The proposed model outperforms the gold-standard model on three methods . the results show that the model performs better on gold-standardized data . |
Overestimation of Syntactic Representation in Neural Language Models (2020.acl-main)
Copied to clipboard
| Challenge: | Several testing methodologies have been developed to probe models’ syntactic representations. |
| Approach: | They propose a method to determine syntactic structure by training a model on strings generated according to a template and testing its ability to distinguish between similar ones with different syntax. |
| Outcome: | The proposed method reproduces positive results with two non-syntactic baseline language models: an n-gram model and an LSTM model trained on scrambled inputs. |
Controlled Language Generation for Language Learning Items (2022.emnlp-industry)
Copied to clipboard
| Challenge: | Recent advances in pre-trained language models have resulted in success in generating fluent English text. |
| Approach: | They propose to employ natural language generation to rapidly generate English language items . they experiment with deep pretrained models and develop methods for controlling items for factors relevant in language learning . |
| Outcome: | The proposed framework shows high grammatically scores for all models and higher complexity over baseline models. |
GRAMMAR-LLM: Grammar-Constrained Natural Language Generation (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to fine-tuning and prompting are insufficient to ensure compliance with predefined taxonomies, syntactic structures, or domain-specific rules. |
| Approach: | They propose a framework that integrates formal grammatical constraints into the decoding process to enforce syntactic correctness in linear time while maintaining expressiveness in grammar rule definition. |
| Outcome: | The proposed framework enforces syntactic correctness in linear time while maintaining expressiveness in grammar rule definition. |
Generating Text from Language Models (2023.acl-tutorials)
Copied to clipboard
| Challenge: | a growing percentage of natural language processing tasks focus on the generation of text from probabilistic language models. |
| Approach: | They will provide a centralized discussion of critical considerations when choosing how to generate from a language model. |
| Outcome: | This tutorial will provide a centralized discussion of critical considerations when choosing how to generate from a language model. |