A Study of Automatic Metrics for the Evaluation of Natural Language Explanations

• URL: http://arxiv.org/abs/2103.08545v1
• Date: Mon, 15 Mar 2021 17:10:39 GMT
• Title: A Study of Automatic Metrics for the Evaluation of Natural Language Explanations
• Authors: Miruna Clinciu, Arash Eshghi, and Helen Hastie
• Abstract summary: We explore parallels between the generation of such explanations and the much-studied field of evaluation of Natural Language Generation (NLG) We present the ExBAN corpus: a crowd-sourced corpus of NL explanations for Bayesian Networks. We find that embedding-based automatic NLG evaluation methods, such as BERTScore and BLEURT, have a higher correlation with human ratings, compared to word-overlap metrics, such as BLEU and ROUGE.
• Score: 1.7205106391379024
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: As transparency becomes key for robotics and AI, it will be necessary to evaluate the methods through which transparency is provided, including automatically generated natural language (NL) explanations. Here, we explore parallels between the generation of such explanations and the much-studied field of evaluation of Natural Language Generation (NLG). Specifically, we investigate which of the NLG evaluation measures map well to explanations. We present the ExBAN corpus: a crowd-sourced corpus of NL explanations for Bayesian Networks. We run correlations comparing human subjective ratings with NLG automatic measures. We find that embedding-based automatic NLG evaluation methods, such as BERTScore and BLEURT, have a higher correlation with human ratings, compared to word-overlap metrics, such as BLEU and ROUGE. This work has implications for Explainable AI and transparent robotic and autonomous systems.

Related papers

• Comparing zero-shot self-explanations with human rationales in multilingual text classification [5.32539007352208]
  Instruction-tuned LLMs generate self-explanations that do not require computations or the application of possibly complex XAI methods. We analyse whether this ability results in a good explanation by evaluating self-explanations in the form of input rationales. Our results show that self-explanations align more closely with human annotations compared to LRP, while maintaining a comparable level of faithfulness.
  arXiv  Detail & Related papers  (2024-10-04T10:14:12Z)
• A LLM-Based Ranking Method for the Evaluation of Automatic Counter-Narrative Generation [14.064465097974836]
  This paper proposes a novel approach to evaluate Counter Narrative (CN) generation using a Large Language Model (LLM) as an evaluator. We show that traditional automatic metrics correlate poorly with human judgements and fail to capture the nuanced relationship between generated CNs and human perception.
  arXiv  Detail & Related papers  (2024-06-21T15:11:33Z)
• Unsupervised Approach to Evaluate Sentence-Level Fluency: Do We Really Need Reference? [3.2528685897001455]
  This paper adapts an existing unsupervised technique for measuring text fluency without the need for any reference. Our approach leverages various word embeddings and trains language models using Recurrent Neural Network (RNN) architectures. To assess the performance of the models, we conduct a comparative analysis across 10 Indic languages.
  arXiv  Detail & Related papers  (2023-12-03T20:09:23Z)
• CLOMO: Counterfactual Logical Modification with Large Language Models [109.60793869938534]
  We introduce a novel task, Counterfactual Logical Modification (CLOMO), and a high-quality human-annotated benchmark. In this task, LLMs must adeptly alter a given argumentative text to uphold a predetermined logical relationship. We propose an innovative evaluation metric, the Self-Evaluation Score (SES), to directly evaluate the natural language output of LLMs.
  arXiv  Detail & Related papers  (2023-11-29T08:29:54Z)
• Situated Natural Language Explanations [54.083715161895036]
  Natural language explanations (NLEs) are among the most accessible tools for explaining decisions to humans. Existing NLE research perspectives do not take the audience into account. Situated NLE provides a perspective and facilitates further research on the generation and evaluation of explanations.
  arXiv  Detail & Related papers  (2023-08-27T14:14:28Z)
• NLG Evaluation Metrics Beyond Correlation Analysis: An Empirical Metric Preference Checklist [20.448405494617397]
  Task-agnostic metrics, such as Perplexity, BLEU, BERTScore, are cost-effective and highly adaptable to diverse NLG tasks. Human-aligned metrics (CTC, CtrlEval, UniEval) improves correlation level by incorporating desirable human-like qualities as training objective. We show that automatic metrics provide a better guidance than human on discriminating system-level performance in Text Summarization and Controlled Generation tasks.
  arXiv  Detail & Related papers  (2023-05-15T11:51:55Z)
• G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment [64.01972723692587]
  We present G-Eval, a framework of using large language models with chain-of-thoughts (CoT) and a form-filling paradigm to assess the quality of NLG outputs. We show that G-Eval with GPT-4 as the backbone model achieves a Spearman correlation of 0.514 with human on summarization task, outperforming all previous methods by a large margin.
  arXiv  Detail & Related papers  (2023-03-29T12:46:54Z)
• 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)
• OpenMEVA: A Benchmark for Evaluating Open-ended Story Generation Metrics [53.779709191191685]
  We propose OpenMEVA, a benchmark for evaluating open-ended story generation metrics. OpenMEVA provides a comprehensive test suite to assess the capabilities of metrics. We observe that existing metrics have poor correlation with human judgments, fail to recognize discourse-level incoherence, and lack inferential knowledge.
  arXiv  Detail & Related papers  (2021-05-19T04:45:07Z)
• Leakage-Adjusted Simulatability: Can Models Generate Non-Trivial Explanations of Their Behavior in Natural Language? [86.60613602337246]
  We introduce a leakage-adjusted simulatability (LAS) metric for evaluating NL explanations. LAS measures how well explanations help an observer predict a model's output, while controlling for how explanations can directly leak the output. We frame explanation generation as a multi-agent game and optimize explanations for simulatability while penalizing label leakage.
  arXiv  Detail & Related papers  (2020-10-08T16:59:07Z)
• Automating Text Naturalness Evaluation of NLG Systems [0.0]
  We present an attempt to automate the evaluation of text naturalness. Instead of relying on human participants for scoring or labeling the text samples, we propose to automate the process. We analyze the text probability fractions and observe how they are influenced by the size of the generative and discriminative models involved in the process.
  arXiv  Detail & Related papers  (2020-06-23T18:48:33Z)

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.