Multi-Dimensional Evaluation of Text Summarization with In-Context Learning

• URL: http://arxiv.org/abs/2306.01200v1
• Date: Thu, 1 Jun 2023 23:27:49 GMT
• Title: Multi-Dimensional Evaluation of Text Summarization with In-Context Learning
• Authors: Sameer Jain, Vaishakh Keshava, Swarnashree Mysore Sathyendra, Patrick Fernandes, Pengfei Liu, Graham Neubig and Chunting Zhou
• Abstract summary: In this paper, we study the efficacy of large language models as multi-dimensional evaluators using in-context learning. Our experiments show that in-context learning-based evaluators are competitive with learned evaluation frameworks for the task of text summarization. We then analyze the effects of factors such as the selection and number of in-context examples on performance.
• Score: 79.02280189976562
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Evaluation of natural language generation (NLG) is complex and multi-dimensional. Generated text can be evaluated for fluency, coherence, factuality, or any other dimensions of interest. Most frameworks that perform such multi-dimensional evaluation require training on large manually or synthetically generated datasets. In this paper, we study the efficacy of large language models as multi-dimensional evaluators using in-context learning, obviating the need for large training datasets. Our experiments show that in-context learning-based evaluators are competitive with learned evaluation frameworks for the task of text summarization, establishing state-of-the-art on dimensions such as relevance and factual consistency. We then analyze the effects of factors such as the selection and number of in-context examples on performance. Finally, we study the efficacy of in-context learning based evaluators in evaluating zero-shot summaries written by large language models such as GPT-3.

Related papers

• Towards More Effective Table-to-Text Generation: Assessing In-Context Learning and Self-Evaluation with Open-Source Models [0.0]
  This study explores the effectiveness of various in-context learning strategies in language models (LMs) across benchmark datasets. We employ a large language model (LLM) self-evaluation approach using chain-of-thought reasoning and assess its correlation with human-aligned metrics like BERTScore. Our findings highlight the significant impact of examples in improving table-to-text generation and suggest that, while LLM self-evaluation has potential, its current alignment with human judgment could be enhanced.
  arXiv  Detail & Related papers  (2024-10-15T09:19:42Z)
• A Comparative Study of Quality Evaluation Methods for Text Summarization [0.5512295869673147]
  This paper proposes a novel method based on large language models (LLMs) for evaluating text summarization. Our results show that LLMs evaluation aligns closely with human evaluation, while widely-used automatic metrics such as ROUGE-2, BERTScore, and SummaC do not and also lack consistency.
  arXiv  Detail & Related papers  (2024-06-30T16:12:37Z)
• SPOR: A Comprehensive and Practical Evaluation Method for Compositional Generalization in Data-to-Text Generation [21.68354181391989]
  We propose SPOR, a comprehensive and practical evaluation method for compositional generalization in data-to-text generation. We demonstrate SPOR on two different datasets and evaluate some existing language models including LLMs.
  arXiv  Detail & Related papers  (2024-05-17T09:25:30Z)
• Exploring Precision and Recall to assess the quality and diversity of LLMs [82.21278402856079]
  We introduce a novel evaluation framework for Large Language Models (LLMs) such as textscLlama-2 and textscMistral. This approach allows for a nuanced assessment of the quality and diversity of generated text without the need for aligned corpora.
  arXiv  Detail & Related papers  (2024-02-16T13:53:26Z)
• Can Large Language Models Understand Context? [17.196362853457412]
  This paper introduces a context understanding benchmark by adapting existing datasets to suit the evaluation of generative models. Experimental results indicate that pre-trained dense models struggle with understanding more nuanced contextual features when compared to state-of-the-art fine-tuned models. As LLM compression holds growing significance in both research and real-world applications, we assess the context understanding of quantized models under in-context-learning settings.
  arXiv  Detail & Related papers  (2024-02-01T18:55:29Z)
• How Well Do Text Embedding Models Understand Syntax? [50.440590035493074]
  The ability of text embedding models to generalize across a wide range of syntactic contexts remains under-explored. Our findings reveal that existing text embedding models have not sufficiently addressed these syntactic understanding challenges. We propose strategies to augment the generalization ability of text embedding models in diverse syntactic scenarios.
  arXiv  Detail & Related papers  (2023-11-14T08:51:00Z)
• DecompEval: Evaluating Generated Texts as Unsupervised Decomposed Question Answering [95.89707479748161]
  Existing evaluation metrics for natural language generation (NLG) tasks face the challenges on generalization ability and interpretability. We propose a metric called DecompEval that formulates NLG evaluation as an instruction-style question answering task. We decompose our devised instruction-style question about the quality of generated texts into the subquestions that measure the quality of each sentence. The subquestions with their answers generated by PLMs are then recomposed as evidence to obtain the evaluation result.
  arXiv  Detail & Related papers  (2023-07-13T16:16:51Z)
• Large Language Models are Diverse Role-Players for Summarization Evaluation [82.31575622685902]
  A document summary's quality can be assessed by human annotators on various criteria, both objective ones like grammar and correctness, and subjective ones like informativeness, succinctness, and appeal. Most of the automatic evaluation methods like BLUE/ROUGE may be not able to adequately capture the above dimensions. We propose a new evaluation framework based on LLMs, which provides a comprehensive evaluation framework by comparing generated text and reference text from both objective and subjective aspects.
  arXiv  Detail & Related papers  (2023-03-27T10:40:59Z)
• Towards a Unified Multi-Dimensional Evaluator for Text Generation [101.47008809623202]
  We propose a unified multi-dimensional evaluator UniEval for Natural Language Generation (NLG) We re-frame NLG evaluation as a Boolean Question Answering (QA) task, and by guiding the model with different questions, we can use one evaluator to evaluate from multiple dimensions. Experiments on three typical NLG tasks show that UniEval correlates substantially better with human judgments than existing metrics.
  arXiv  Detail & Related papers  (2022-10-13T17:17:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.