Learning Evaluation Models from Large Language Models for Sequence Generation

• URL: http://arxiv.org/abs/2308.04386v2
• Date: Tue, 25 Mar 2025 12:00:54 GMT
• Title: Learning Evaluation Models from Large Language Models for Sequence Generation
• Authors: Chenglong Wang, Hang Zhou, Kaiyan Chang, Tongran Liu, Chunliang Zhang, Quan Du, Tong Xiao, Yue Zhang, Jingbo Zhu,
• Abstract summary: We propose a three-stage evaluation model training method that utilizes large language models to generate labeled data for model-based metric development.<n> Experimental results on the SummEval benchmark demonstrate that CSEM can effectively train an evaluation model without human-labeled data.
• Score: 61.8421748792555
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automatic evaluation of sequence generation, traditionally reliant on metrics like BLEU and ROUGE, often fails to capture the semantic accuracy of generated text sequences due to their emphasis on n-gram overlap. A promising solution to this problem is to develop model-based metrics, such as BLEURT and COMET. However, these approaches are typically hindered by the scarcity of labeled evaluation data, which is necessary to train the evaluation models. In this work, we build upon this challenge by proposing the Customized Sequence Evaluation Metric (CSEM), a three-stage evaluation model training method that utilizes large language models to generate labeled data for model-based metric development, thereby eliminating the need for human-labeled data. Additionally, we expand the scope of CSEM to support various evaluation types, including single-aspect, multi-aspect, reference-free, and reference-based evaluations, enabling the customization of metrics to suit diverse real-world scenarios. Experimental results on the SummEval benchmark demonstrate that CSEM can effectively train an evaluation model without human-labeled data. Further experiments in reinforcement learning and reranking show that metrics developed through CSEM outperform traditional evaluation metrics, leading to substantial improvements in sequence quality as evaluated by both commonly used metrics and ChatGPT.

Related papers

• DSGram: Dynamic Weighting Sub-Metrics for Grammatical Error Correction in the Era of Large Language Models [39.493913608472404]
  Large language model (LLM)-based Grammatical Error Correction (GEC) models often produce corrections that diverge from provided gold references. This discrepancy undermines the reliability of traditional reference-based evaluation metrics. We propose a novel evaluation framework for GEC models, DSGram, integrating Semantic Coherence, Edit Level, and Fluency, and utilizing a dynamic weighting mechanism.
  arXiv  Detail & Related papers  (2024-12-17T11:54:16Z)
• Relation-based Counterfactual Data Augmentation and Contrastive Learning for Robustifying Natural Language Inference Models [0.0]
  We propose a method in which we use token-based and sentence-based augmentation methods to generate counterfactual sentence pairs. We show that the proposed method can improve the performance and robustness of the NLI model.
  arXiv  Detail & Related papers  (2024-10-28T03:43:25Z)
• Benchmarks as Microscopes: A Call for Model Metrology [76.64402390208576]
  Modern language models (LMs) pose a new challenge in capability assessment. To be confident in our metrics, we need a new discipline of model metrology.
  arXiv  Detail & Related papers  (2024-07-22T17:52:12Z)
• QualEval: Qualitative Evaluation for Model Improvement [82.73561470966658]
  We propose QualEval, which augments quantitative scalar metrics with automated qualitative evaluation as a vehicle for model improvement. QualEval uses a powerful LLM reasoner and our novel flexible linear programming solver to generate human-readable insights. We demonstrate that leveraging its insights, for example, improves the absolute performance of the Llama 2 model by up to 15% points relative.
  arXiv  Detail & Related papers  (2023-11-06T00:21:44Z)
• Don't Make Your LLM an Evaluation Benchmark Cheater [142.24553056600627]
  Large language models(LLMs) have greatly advanced the frontiers of artificial intelligence, attaining remarkable improvement in model capacity. To assess the model performance, a typical approach is to construct evaluation benchmarks for measuring the ability level of LLMs. We discuss the potential risk and impact of inappropriately using evaluation benchmarks and misleadingly interpreting the evaluation results.
  arXiv  Detail & Related papers  (2023-11-03T14:59:54Z)
• Bring Your Own Data! Self-Supervised Evaluation for Large Language Models [52.15056231665816]
  We propose a framework for self-supervised evaluation of Large Language Models (LLMs) We demonstrate self-supervised evaluation strategies for measuring closed-book knowledge, toxicity, and long-range context dependence. We find strong correlations between self-supervised and human-supervised evaluations.
  arXiv  Detail & Related papers  (2023-06-23T17:59:09Z)
• SimOAP: Improve Coherence and Consistency in Persona-based Dialogue Generation via Over-sampling and Post-evaluation [54.66399120084227]
  Language models trained on large-scale corpora can generate remarkably fluent results in open-domain dialogue. For the persona-based dialogue generation task, consistency and coherence are great challenges for language models. A two-stage SimOAP strategy is proposed, i.e., over-sampling and post-evaluation.
  arXiv  Detail & Related papers  (2023-05-18T17:23:00Z)
• Feature Likelihood Divergence: Evaluating the Generalization of Generative Models Using Samples [25.657798631897908]
  Feature Likelihood Divergence provides a comprehensive trichotomic evaluation of generative models. We empirically demonstrate the ability of FLD to identify overfitting problem cases, even when previously proposed metrics fail.
  arXiv  Detail & Related papers  (2023-02-09T04:57:27Z)
• Few-shot Subgoal Planning with Language Models [58.11102061150875]
  We show that language priors encoded in pre-trained language models allow us to infer fine-grained subgoal sequences. In contrast to recent methods which make strong assumptions about subgoal supervision, our experiments show that language models can infer detailed subgoal sequences without any fine-tuning.
  arXiv  Detail & Related papers  (2022-05-28T01:03:30Z)
• A Generative Language Model for Few-shot Aspect-Based Sentiment Analysis [90.24921443175514]
  We focus on aspect-based sentiment analysis, which involves extracting aspect term, category, and predicting their corresponding polarities. We propose to reformulate the extraction and prediction tasks into the sequence generation task, using a generative language model with unidirectional attention. Our approach outperforms the previous state-of-the-art (based on BERT) on average performance by a large margins in few-shot and full-shot settings.
  arXiv  Detail & Related papers  (2022-04-11T18:31:53Z)
• Reconsidering the Past: Optimizing Hidden States in Language Models [35.7524942657169]
  We present Hidden-State Optimization (HSO), a gradient-based method for improving the performance of transformer language models. HSO computes the gradient of the log-probability the language model assigns to an evaluation text, but uses it to update the cached hidden states rather than the model parameters.
  arXiv  Detail & Related papers  (2021-12-16T06:14:37Z)
• Improving Non-autoregressive Generation with Mixup Training [51.61038444990301]
  We present a non-autoregressive generation model based on pre-trained transformer models. We propose a simple and effective iterative training method called MIx Source and pseudo Target. Our experiments on three generation benchmarks including question generation, summarization and paraphrase generation, show that the proposed framework achieves the new state-of-the-art results.
  arXiv  Detail & Related papers  (2021-10-21T13:04:21Z)
• Maximizing Efficiency of Language Model Pre-training for Learning Representation [6.518508607788086]
  ELECTRA is a novel approach for improving the compute efficiency of pre-trained language models. Our work proposes adaptive early exit strategy to maximize the efficiency of the pre-training process.
  arXiv  Detail & Related papers  (2021-10-13T10:25:06Z)
• How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models [95.8037674226622]
  We introduce a 3-dimensional evaluation metric that characterizes the fidelity, diversity and generalization performance of any generative model in a domain-agnostic fashion. Our metric unifies statistical divergence measures with precision-recall analysis, enabling sample- and distribution-level diagnoses of model fidelity and diversity.
  arXiv  Detail & Related papers  (2021-02-17T18:25:30Z)
• Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-Training [86.91380874390778]
  We present Generation-Augmented Pre-training (GAP), that jointly learns representations of natural language utterances and table schemas by leveraging generation models to generate pre-train data. Based on experimental results, neural semantics that leverage GAP MODEL obtain new state-of-the-art results on both SPIDER and CRITERIA-TO-generative benchmarks.
  arXiv  Detail & Related papers  (2020-12-18T15:53:50Z)
• Overestimation of Syntactic Representationin Neural Language Models [16.765097098482286]
  One popular method for determining a model's ability to induce syntactic structure trains a model on strings generated according to a template then tests the model's ability to distinguish such strings from superficially similar ones with different syntax. We illustrate a fundamental problem with this approach by reproducing positive results from a recent paper with two non-syntactic baseline language models.
  arXiv  Detail & Related papers  (2020-04-10T15:13:03Z)
• Learning by Semantic Similarity Makes Abstractive Summarization Better [13.324006587838522]
  We compare the generated summaries from recent LM, BART, and the reference summaries from a benchmark dataset, CNN/DM. Interestingly, model-generated summaries receive higher scores relative to reference summaries.
  arXiv  Detail & Related papers  (2020-02-18T17:59:02Z)
• Learning to Compare for Better Training and Evaluation of Open Domain Natural Language Generation Models [23.62054164511058]
  We propose to evaluate natural language generation models by learning to compare a pair of generated sentences by fine-tuning BERT. While able to be trained in a fully self-supervised fashion, our model can be further fine-tuned with a little amount of human preference annotation.
  arXiv  Detail & Related papers  (2020-02-12T15:52:21Z)

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.