Pragmatics in the Era of Large Language Models: A Survey on Datasets, Evaluation, Opportunities and Challenges

• URL: http://arxiv.org/abs/2502.12378v1
• Date: Mon, 17 Feb 2025 23:31:38 GMT
• Title: Pragmatics in the Era of Large Language Models: A Survey on Datasets, Evaluation, Opportunities and Challenges
• Authors: Bolei Ma, Yuting Li, Wei Zhou, Ziwei Gong, Yang Janet Liu, Katja Jasinskaja, Annemarie Friedrich, Julia Hirschberg, Frauke Kreuter, Barbara Plank,
• Abstract summary: We provide a review of resources designed for evaluating pragmatic capabilities in NLP. We analyze task designs, data collection methods, evaluation approaches, and their relevance to real-world applications. Our survey aims to clarify the landscape of pragmatic evaluation and guide the development of more comprehensive and targeted benchmarks.
• Score: 26.846627984383836
• License:
• Abstract: Understanding pragmatics-the use of language in context-is crucial for developing NLP systems capable of interpreting nuanced language use. Despite recent advances in language technologies, including large language models, evaluating their ability to handle pragmatic phenomena such as implicatures and references remains challenging. To advance pragmatic abilities in models, it is essential to understand current evaluation trends and identify existing limitations. In this survey, we provide a comprehensive review of resources designed for evaluating pragmatic capabilities in NLP, categorizing datasets by the pragmatics phenomena they address. We analyze task designs, data collection methods, evaluation approaches, and their relevance to real-world applications. By examining these resources in the context of modern language models, we highlight emerging trends, challenges, and gaps in existing benchmarks. Our survey aims to clarify the landscape of pragmatic evaluation and guide the development of more comprehensive and targeted benchmarks, ultimately contributing to more nuanced and context-aware NLP models.

Related papers

• LLMs for Generalizable Language-Conditioned Policy Learning under Minimal Data Requirements [50.544186914115045]
  This paper presents TEDUO, a novel training pipeline for offline language-conditioned policy learning. TEDUO operates on easy-to-obtain, unlabeled datasets and is suited for the so-called in-the-wild evaluation, wherein the agent encounters previously unseen goals and states.
  arXiv  Detail & Related papers  (2024-12-09T18:43:56Z)
• Open-ended VQA benchmarking of Vision-Language models by exploiting Classification datasets and their semantic hierarchy [27.454549324141087]
  We propose a novel VQA benchmark based on well-known visual classification datasets. We also suggest using the semantic hierarchy of the label space to ask automatically generated follow-up questions about the ground-truth category. Our contributions aim to lay the foundation for more precise and meaningful assessments.
  arXiv  Detail & Related papers  (2024-02-11T18:26:18Z)
• 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)
• Leveraging Large Language Models for NLG Evaluation: Advances and Challenges [57.88520765782177]
  Large Language Models (LLMs) have opened new avenues for assessing generated content quality, e.g., coherence, creativity, and context relevance. We propose a coherent taxonomy for organizing existing LLM-based evaluation metrics, offering a structured framework to understand and compare these methods. By discussing unresolved challenges, including bias, robustness, domain-specificity, and unified evaluation, this paper seeks to offer insights to researchers and advocate for fairer and more advanced NLG evaluation techniques.
  arXiv  Detail & Related papers  (2024-01-13T15:59:09Z)
• Natural Language Processing for Dialects of a Language: A Survey [56.93337350526933]
  State-of-the-art natural language processing (NLP) models are trained on massive training corpora, and report a superlative performance on evaluation datasets. This survey delves into an important attribute of these datasets: the dialect of a language. Motivated by the performance degradation of NLP models for dialectal datasets and its implications for the equity of language technologies, we survey past research in NLP for dialects in terms of datasets, and approaches.
  arXiv  Detail & Related papers  (2024-01-11T03:04:38Z)
• Ling-CL: Understanding NLP Models through Linguistic Curricula [17.44112549879293]
  We employ a characterization of linguistic complexity from psycholinguistic and language acquisition research. We develop data-driven curricula to understand the underlying linguistic knowledge that models learn to address NLP tasks.
  arXiv  Detail & Related papers  (2023-10-31T01:44:33Z)
• Disco-Bench: A Discourse-Aware Evaluation Benchmark for Language Modelling [70.23876429382969]
  We propose a benchmark that can evaluate intra-sentence discourse properties across a diverse set of NLP tasks. Disco-Bench consists of 9 document-level testsets in the literature domain, which contain rich discourse phenomena. For linguistic analysis, we also design a diagnostic test suite that can examine whether the target models learn discourse knowledge.
  arXiv  Detail & Related papers  (2023-07-16T15:18:25Z)
• Exploring Large Language Model for Graph Data Understanding in Online Job Recommendations [63.19448893196642]
  We present a novel framework that harnesses the rich contextual information and semantic representations provided by large language models to analyze behavior graphs. By leveraging this capability, our framework enables personalized and accurate job recommendations for individual users.
  arXiv  Detail & Related papers  (2023-07-10T11:29:41Z)
• Interactive Natural Language Processing [67.87925315773924]
  Interactive Natural Language Processing (iNLP) has emerged as a novel paradigm within the field of NLP. This paper offers a comprehensive survey of iNLP, starting by proposing a unified definition and framework of the concept.
  arXiv  Detail & Related papers  (2023-05-22T17:18:29Z)
• Towards explainable evaluation of language models on the semantic similarity of visual concepts [0.0]
  We examine the behavior of high-performing pre-trained language models, focusing on the task of semantic similarity for visual vocabularies. First, we address the need for explainable evaluation metrics, necessary for understanding the conceptual quality of retrieved instances. Secondly, adversarial interventions on salient query semantics expose vulnerabilities of opaque metrics and highlight patterns in learned linguistic representations.
  arXiv  Detail & Related papers  (2022-09-08T11:40:57Z)
• Curriculum: A Broad-Coverage Benchmark for Linguistic Phenomena in Natural Language Understanding [1.827510863075184]
  Curriculum is a new format of NLI benchmark for evaluation of broad-coverage linguistic phenomena. We show that this linguistic-phenomena-driven benchmark can serve as an effective tool for diagnosing model behavior and verifying model learning quality.
  arXiv  Detail & Related papers  (2022-04-13T10:32:03Z)

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.