| Challenge: | Language models generate fluent text, but it remains unclear whether output retains coherent high-level structure. |
| Approach: | They propose to use a statistical tool to evaluate high-level structure of text . they compare distributions between real and generated data in latent space . |
| Outcome: | The proposed model criticism compares distributions between real and generated data in a latent space . different generative processes identify specific failure modes of the underlying model . |
Similar Papers
RSTGen: Imbuing Fine-Grained Interpretable Control into Long-FormText Generators (2022.naacl-main)
Copied to clipboard
| Challenge: | Using a framework based on Rhetorical Structure Theory, we aim to improve the cohesion and coherence of long-form text generated by language models. |
| Approach: | They propose a framework that utilises Rhetorical Structure Theory to control the discourse structure, semantics and topics of generated text. |
| Outcome: | The proposed framework performs competitively against existing models while offering significantly more controls over generated text than alternative methods. |
CritiqueLLM: Towards an Informative Critique Generation Model for Evaluation of Large Language Model Generation (2024.acl-long)
Copied to clipboard
Pei Ke, Bosi Wen, Andrew Feng, Xiao Liu, Xuanyu Lei, Jiale Cheng, Shengyuan Wang, Aohan Zeng, Yuxiao Dong, Hongning Wang, Jie Tang, Minlie Huang
| Challenge: | Existing models for NLP evaluations lack the ability to generate informative critiques in pointwise grading and pairwise comparison especially without references. |
| Approach: | They propose a method which can acquire pointwise grading critiques with pseudo references and revise these critiques via multi-path prompting to obtain informative evaluation data in different tasks and settings. |
| Outcome: | The proposed method outperforms all open-source models and even GPT-4 in system-level correlations of pointwise grading. |
Faithful Persona-based Conversational Dataset Generation with Large Language Models (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing datasets for training conversational AI models do not sufficiently model their users. |
| Approach: | They propose a generator-critic architecture framework to expand the initial dataset while improving the quality of its conversations. |
| Outcome: | The proposed framework expands the initial dataset while improving the quality of its conversations. |
Strategies for Structuring Story Generation (P19-1)
Copied to clipboard
| Challenge: | Existing language models generate word by word, but fail to capture high-level interactions . a novel decomposition approach allows more abstract representations to be generated first . |
| Approach: | They propose models which abstract over actions and entities to create stories . they generate predicate-argument structure, then replace placeholders with context-sensitive names . |
| Outcome: | The proposed models improve diversity and coherence of events and entities in generated stories. |
Capabilities and Evaluation Biases of Large Language Models in Classical Chinese Poetry Generation: A Case Study on Tang Poetry (2026.findings-acl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) are increasingly applied to creative domains, yet performance in classical Chinese poetry generation and evaluation remains poorly understood. |
| Approach: | They propose a framework that combines computational metrics, LLM-as-a-judge assessment, and human expert validation to evaluate large language models. |
| Outcome: | The proposed framework evaluates state-of-the-art LLMs across multiple dimensions of poetic quality in Tang poetry generation. |
SuperWriter: Reflection-Driven Long-Form Generation with Large Language Models (2026.findings-acl)
Copied to clipboard
| Challenge: | Long-form text generation remains a challenge for large language models . generating extended sequences often leads to degraded coherence and logical consistency . |
| Approach: | They propose a framework that integrates explicit structured thinking into long-form text generation. |
| Outcome: | The proposed framework surpasses even larger-scale models in evaluation and human evaluation. |
GRUEN for Evaluating Linguistic Quality of Generated Text (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing evaluation metrics focus on content selection, not linguistic quality . proposed GRUEN measures Grammaticality, non-redundancy, focUs, structure and coherence of generated text. |
| Approach: | They propose to use a BERT-based model and a class of syntactic, semantic, and contextual features to examine the system output. |
| Outcome: | Experiments show that the proposed metric correlates highly with human judgments. |
Text-to-Text Automatic Story Generation: A Survey (2026.eacl-srw)
Copied to clipboard
| Challenge: | Automated story generation aims to produce coherent, engaging, and contextually consistent narratives with minimal or no human involvement . despite advances in large language models, maintaining narrative coherence, character consistency, storyline diversity, and plot controllability in generating stories is still challenging. |
| Approach: | They propose to develop new evaluation metrics and better data sets to support automatic story generation. |
| Outcome: | The proposed evaluation metrics and better datasets will improve narrative coherence and consistency and explore practical applications of story generation. |
Are Large Language Models Capable of Generating Human-Level Narratives? (2024.emnlp-main)
Copied to clipboard
Yufei Tian, Tenghao Huang, Miri Liu, Derek Jiang, Alexander Spangher, Muhao Chen, Jonathan May, Nanyun Peng
| Challenge: | a recent HCI study has pointed to gaps in machine storytelling ability at the global level . authors show that LLMs have less suspense and less tension than human stories . |
| Approach: | They propose a computational framework to analyze narratives through three discourse-level aspects. |
| Outcome: | The proposed framework analyzes narratives through three discourse-level aspects . it shows that LLMs fall short of human abilities in discourse understanding . |
Leveraging Large Language Models for NLG Evaluation: Advances and Challenges (2024.emnlp-main)
Copied to clipboard
| Challenge: | introducing Large Language Models (LLMs) has opened new avenues for assessing generated content quality, e.g., coherence, creativity, and context relevance. |
| Approach: | They propose a taxonomy for organizing existing LLM-based evaluation metrics and a structured framework to understand and compare them. |
| Outcome: | The proposed taxonomy offers a framework to understand and compare LLM-based evaluation methods. |