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 pragmatic 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.

In table question answering (TQA), tables are encoded as either texts or images. Prior work suggests that passing images of tables to multi-modal large language models (MLLMs) performs comparably to using textual input with large language models (LLMs). However, the lack of controlled setups limits fine-grained distinctions between these approaches. In this paper, we conduct the first controlled study on the effectiveness of several combinations of table representations and model types from two perspectives: question complexity and table size. We build a new benchmark based on existing TQA datasets. In a systematic analysis of seven pairs of MLLMs and LLMs, we find that the best combination of table representation and model varies across setups. We propose FRES, a method selecting table representations dynamically, and observe a 10% average performance improvement compared to using both representations indiscriminately.

Tables can be represented either as text or as images. Previous works on table question answering (TQA) typically rely on only one representation, neglecting the potential benefits of combining both. In this work, we explore integrating textual and visual table representations using multi-modal large language models (MLLMs) for TQA. Specifically, we propose RITT, a retrieval-assisted framework that first identifies the most relevant part of a table for a given question, then dynamically selects the optimal table representations based on the question type. Experiments demonstrate that our framework significantly outperforms the baseline MLLMs by an average of 13 Exact Match and surpasses two text-only state-of-the-art TQA methods on four TQA benchmarks, highlighting the benefits of leveraging both textual and visual table representations.