Emphasising Structured Information: Integrating Abstract Meaning Representation into LLMs for Enhanced Open-Domain Dialogue Evaluation

• URL: http://arxiv.org/abs/2404.01129v3
• Date: Fri, 16 Aug 2024 10:03:53 GMT
• Title: Emphasising Structured Information: Integrating Abstract Meaning Representation into LLMs for Enhanced Open-Domain Dialogue Evaluation
• Authors: Bohao Yang, Kun Zhao, Chen Tang, Dong Liu, Liang Zhan, Chenghua Lin,
• Abstract summary: This paper proposes an effective framework for open-domain dialogue evaluation. It combines domain-specific language models (SLMs) enhanced with Abstract Meaning Representation (AMR) knowledge with Large Language Models (LLMs) Experimental results on open-domain dialogue evaluation tasks demonstrate the superiority of our method compared to a wide range of state-of-the-art baselines.
• Score: 26.330012489735456
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automatic open-domain dialogue evaluation has attracted increasing attention. Trainable evaluation metrics, typically trained with true positive and randomly selected negative responses, tend to assign higher scores to responses that share greater content similarity with a given context. However, adversarial negative responses, despite possessing high content similarity with the contexts, are semantically different. Consequently, existing evaluation metrics are not robust enough to evaluate such responses, resulting in low correlations with human judgments. While recent studies have demonstrated the effectiveness of Large Language Models (LLMs) for open-domain dialogue evaluation, they still face challenges in effectively handling adversarial negative examples. In this paper, we propose an effective framework for open-domain dialogue evaluation, which combines domain-specific language models (SLMs) enhanced with Abstract Meaning Representation (AMR) knowledge with LLMs. The SLMs can explicitly incorporate AMR graph information of the dialogue through a gating mechanism for enhanced dialogue semantic representation learning. Both the evaluation result from the SLMs and the AMR graph information are incorporated into the LLM's prompt for enhanced evaluation performance. Experimental results on open-domain dialogue evaluation tasks demonstrate the superiority of our method compared to a wide range of state-of-the-art baselines, especially in discriminating adversarial negative responses. Our code and data are publicly available at https://github.com/Bernard-Yang/SIMAMR.

Related papers

• RAG-Zeval: Towards Robust and Interpretable Evaluation on RAG Responses through End-to-End Rule-Guided Reasoning [64.46921169261852]
  RAG-Zeval is a novel end-to-end framework that formulates faithfulness and correctness evaluation as a rule-guided reasoning task.<n>Our approach trains evaluators with reinforcement learning, facilitating compact models to generate comprehensive and sound assessments.<n>Experiments demonstrate RAG-Zeval's superior performance, achieving the strongest correlation with human judgments.
  arXiv  Detail & Related papers  (2025-05-28T14:55:33Z)
• Learning to Align Multi-Faceted Evaluation: A Unified and Robust Framework [61.38174427966444]
  Large Language Models (LLMs) are being used more and more extensively for automated evaluation in various scenarios.<n>Previous studies have attempted to fine-tune open-source LLMs to replicate the evaluation explanations and judgments of powerful proprietary models.<n>We propose a novel evaluation framework, ARJudge, that adaptively formulates evaluation criteria and synthesizes both text-based and code-driven analyses.
  arXiv  Detail & Related papers  (2025-02-26T06:31:45Z)
• Dynamic benchmarking framework for LLM-based conversational data capture [0.0]
  This paper introduces a benchmarking framework to assess large language models (LLMs) It integrates generative agent simulation to evaluate performance on key dimensions: information extraction, context awareness, and adaptive engagement. Results show that adaptive strategies improve data extraction accuracy, especially when handling ambiguous responses.
  arXiv  Detail & Related papers  (2025-02-04T15:47:47Z)
• On the Benchmarking of LLMs for Open-Domain Dialogue Evaluation [8.672875654352689]
  Large Language Models (LLMs) have showcased remarkable capabilities in various Natural Language Processing tasks. This paper critically examines current evaluation benchmarks, highlighting that the use of older response generators and quality aspects fail to accurately reflect modern chatbots capabilities.
  arXiv  Detail & Related papers  (2024-07-04T11:14:47Z)
• SLIDE: A Framework Integrating Small and Large Language Models for Open-Domain Dialogues Evaluation [23.203761925540736]
  We propose a novel framework SLIDE (Small and Large Integrated for Dialogue Evaluation) Our approach achieves state-of-the-art performance in both the classification and evaluation tasks, and additionally the SLIDE exhibits better correlation with human evaluators.
  arXiv  Detail & Related papers  (2024-05-24T20:32:49Z)
• Context Does Matter: Implications for Crowdsourced Evaluation Labels in Task-Oriented Dialogue Systems [57.16442740983528]
  Crowdsourced labels play a crucial role in evaluating task-oriented dialogue systems. Previous studies suggest using only a portion of the dialogue context in the annotation process. This study investigates the influence of dialogue context on annotation quality.
  arXiv  Detail & Related papers  (2024-04-15T17:56:39Z)
• MATEval: A Multi-Agent Discussion Framework for Advancing Open-Ended Text Evaluation [22.19073789961769]
  generative Large Language Models (LLMs) have been remarkable, however, the quality of the text generated by these models often reveals persistent issues. We propose the MATEval: A "Multi-Agent Text Evaluation framework" Our framework incorporates self-reflection and Chain-of-Thought strategies, along with feedback mechanisms, to enhance the depth and breadth of the evaluation process.
  arXiv  Detail & Related papers  (2024-03-28T10:41:47Z)
• Attribute Structuring Improves LLM-Based Evaluation of Clinical Text Summaries [56.31117605097345]
  Large language models (LLMs) have shown the potential to generate accurate clinical text summaries, but still struggle with issues regarding grounding and evaluation.<n>Here, we explore a general mitigation framework using Attribute Structuring (AS), which structures the summary evaluation process.<n>AS consistently improves the correspondence between human annotations and automated metrics in clinical text summarization.
  arXiv  Detail & Related papers  (2024-03-01T21:59:03Z)
• Exploring the Factual Consistency in Dialogue Comprehension of Large Language Models [51.75805497456226]
  This work focuses on the factual consistency issue with the help of the dialogue summarization task. Our evaluation shows that, on average, 26.8% of the summaries generated by LLMs contain factual inconsistency. To stimulate and enhance the dialogue comprehension ability of LLMs, we propose a fine-tuning paradigm with auto-constructed multi-task data.
  arXiv  Detail & Related papers  (2023-11-13T09:32:12Z)
• Simple LLM Prompting is State-of-the-Art for Robust and Multilingual Dialogue Evaluation [7.767020408405403]
  We propose a novel framework that takes advantage of the strengths of current evaluation models with the newly-established paradigm of prompting Large Language Models (LLMs) Empirical results show our framework achieves state of the art results in terms of mean Spearman correlation scores across several benchmarks.
  arXiv  Detail & Related papers  (2023-08-31T15:19:28Z)
• 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)
• Evaluate What You Can't Evaluate: Unassessable Quality for Generated Response [56.25966921370483]
  There are challenges in using reference-free evaluators based on large language models. Reference-free evaluators are more suitable for open-ended examples with different semantics responses. There are risks in using eference-free evaluators based on LLMs to evaluate the quality of dialogue responses.
  arXiv  Detail & Related papers  (2023-05-24T02:52:48Z)
• Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Models [115.7508325840751]
  The recent success of large language models (LLMs) has shown great potential to develop more powerful conversational recommender systems (CRSs) In this paper, we embark on an investigation into the utilization of ChatGPT for conversational recommendation, revealing the inadequacy of the existing evaluation protocol. We propose an interactive Evaluation approach based on LLMs named iEvaLM that harnesses LLM-based user simulators.
  arXiv  Detail & Related papers  (2023-05-22T15:12:43Z)
• DEAM: Dialogue Coherence Evaluation using AMR-based Semantic Manipulations [46.942369532632604]
  We propose a Dialogue Evaluation metric that relies on AMR-based semantic manipulations for incoherent data generation. Our experiments show that DEAM achieves higher correlations with human judgments compared to baseline methods.
  arXiv  Detail & Related papers  (2022-03-18T03:11:35Z)
• Synthesizing Adversarial Negative Responses for Robust Response Ranking and Evaluation [34.52276336319678]
  Open-domain neural dialogue models have achieved high performance in response ranking and evaluation tasks. Over-reliance on content similarity makes the models less sensitive to the presence of inconsistencies. We propose approaches for automatically creating adversarial negative training data.
  arXiv  Detail & Related papers  (2021-06-10T16:20:55Z)
• Towards Automatic Evaluation of Dialog Systems: A Model-Free Off-Policy Evaluation Approach [84.02388020258141]
  We propose a new framework named ENIGMA for estimating human evaluation scores based on off-policy evaluation in reinforcement learning. ENIGMA only requires a handful of pre-collected experience data, and therefore does not involve human interaction with the target policy during the evaluation. Our experiments show that ENIGMA significantly outperforms existing methods in terms of correlation with human evaluation scores.
  arXiv  Detail & Related papers  (2021-02-20T03:29:20Z)
• Learning an Unreferenced Metric for Online Dialogue Evaluation [53.38078951628143]
  We propose an unreferenced automated evaluation metric that uses large pre-trained language models to extract latent representations of utterances. We show that our model achieves higher correlation with human annotations in an online setting, while not requiring true responses for comparison during inference.
  arXiv  Detail & Related papers  (2020-05-01T20:01:39Z)

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.