Themis: A Reference-free NLG Evaluation Language Model with Flexibility and Interpretability (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for evaluation of natural language generation tasks lack reliable data. |
| Approach: | They propose to use annotations from human and GPT-4 to construct a corpus for NLG evaluation. |
| Outcome: | The proposed corpus can perform flexible and interpretable evaluations without references and surpasses existing models. |
Similar Papers
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. |
G-Eval: NLG Evaluation using Gpt-4 with Better Human Alignment (2023.emnlp-main)
Copied to clipboard
| Challenge: | Conventional reference-based metrics have low correlation with human judgments, especially for open-ended generation tasks. |
| Approach: | They propose to use large language models as reference-free NLG evaluators to assess the quality of NLG outputs. |
| Outcome: | The proposed framework outperforms all previous methods in two generation tasks, and has a Spearman correlation of 0.514 with human on summarization task, and a large variance in human judgments. |
A Tutorial on Evaluation Metrics used in Natural Language Generation (2021.naacl-tutorials)
Copied to clipboard
| Challenge: | This tutorial presents the evolution of automatic evaluation metrics to their current state along with emerging trends in this field. |
| Approach: | This tutorial presents the evolution of automatic evaluation metrics to their current state . it aims to assess the extent of scientific progress made and identify areas/components that need improvement . |
| Outcome: | This tutorial presents the evolution of automatic evaluation metrics to their current state along with emerging trends in this field. |
Semantic-Eval : A Semantic Comprehension Evaluation Framework for Large Language Models Generation without Training (2025.acl-long)
Copied to clipboard
| Challenge: | Large language models (LLMs) have emerged as key drivers of progress in the field of natural language processing. |
| Approach: | They propose a framework that assesses LLM-generated text based on semantic understanding. |
| Outcome: | The proposed framework surpasses traditional evaluation metrics and lags behind GPT-4. |
Navigating the Modern Evaluation Landscape: Considerations in Benchmarks and Frameworks for Large Language Models (LLMs) (2024.lrec-tutorials)
Copied to clipboard
| Challenge: | General-purpose Language Models have changed the world of Natural Language Processing, if not the world itself. |
| Approach: | This tutorial will lay the foundations and explain the basics of evaluation and compare traditional methods to newly developed methods. |
| Outcome: | The tutorial assumes little familiarity with metrics, datasets, prompts and benchmarks . it will compare traditional methods to newly developed methods . |
Deconstructing NLG Evaluation: Evaluation Practices, Assumptions, and Their Implications (2022.naacl-main)
Copied to clipboard
| Challenge: | Evaluating natural language generation systems is difficult, as there are many ways to express similar things in text. |
| Approach: | They combine interviews with NLG practitioners to examine ethical considerations and their implications for NLG evaluation. |
| Outcome: | The findings of the study surface goals, community practices, assumptions, and constraints that shape NLG evaluations, and examine their implications and how they embody ethical considerations. |
Evaluation Metrics in the Era of GPT-4: Reliably Evaluating Large Language Models on Sequence to Sequence Tasks (2023.emnlp-main)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) evaluation is a patchy and inconsistent landscape . established automatic evaluation metrics are poor surrogates, correlating weakly with human judgement. |
| Approach: | They propose to use both automatic and human evaluation to evaluate generative LLMs on three NLP benchmarks: text summarisation, text simplification and grammatical error correction. |
| Outcome: | The proposed model outperforms many popular models according to human reviewers on the majority of metrics, while scoring much worse when using classic automatic evaluation metrics. |
NLG-Metricverse: An End-to-End Library for Evaluating Natural Language Generation (2022.coling-1)
Copied to clipboard
| Challenge: | Natural language generation models are a key component of deep learning, says aaron eliott . he says it is crucial to develop and apply better metrics for NLG evaluation . |
| Approach: | a new open-source library for NLG evaluation is created to facilitate researchers to judge the effectiveness of their models. the framework provides a living collection of NLG metrics in a unified and easy-to-use environment. |
| Outcome: | a new open-source library for NLG evaluation aims to improve performance of models . the framework provides tools to apply, analyze, compare, and visualize the metrics . |
Not All Metrics Are Guilty: Improving NLG Evaluation by Diversifying References (2024.naacl-long)
Copied to clipboard
Tianyi Tang, Hongyuan Lu, Yuchen Jiang, Haoyang Huang, Dongdong Zhang, Xin Zhao, Tom Kocmi, Furu Wei
| Challenge: | Existing evaluation benchmarks with limited references may not accurately reflect the quality of the model’s hypotheses. |
| Approach: | They propose a method to enrich evaluation benchmarks by diversifying the expression of a single reference into multiple high-quality ones to cover the semantic space of the reference sentence as much as possible. |
| Outcome: | The proposed method can enhance evaluation benchmarks by diversifying the expression of reference into multiple high-quality ones to cover the semantic space of the reference sentence as much as possible. |
Learning to Judge: LLMs Designing and Applying Evaluation Rubrics (2026.findings-eacl)
Copied to clipboard
| Challenge: | Large language models are increasingly used as evaluators for natural language generation . human rubrics are often static and misaligned with how models internally represent language quality. |
| Approach: | They propose to use large language models to generate interpretable and task-aware evaluation dimensions and apply them within models. |
| Outcome: | The proposed model improves the semantic coherence and scoring reliability of LLM-defined criteria and their alignment with human criteria. |