VISLA Benchmark: Evaluating Embedding Sensitivity to Semantic and Lexical Alterations

• URL: http://arxiv.org/abs/2404.16365v1
• Date: Thu, 25 Apr 2024 07:08:00 GMT
• Title: VISLA Benchmark: Evaluating Embedding Sensitivity to Semantic and Lexical Alterations
• Authors: Sri Harsha Dumpala, Aman Jaiswal, Chandramouli Sastry, Evangelos Milios, Sageev Oore, Hassan Sajjad,
• Abstract summary: This paper introduces the VISLA benchmark, designed to evaluate the semantic and lexical understanding of language models. An evaluation involving 34 vision-language models (VLMs) and 20 unimodal language models (ULMs) reveals surprising difficulties in distinguishing between lexical and semantic variations.
• Score: 13.608653575298183
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite their remarkable successes, state-of-the-art language models face challenges in grasping certain important semantic details. This paper introduces the VISLA (Variance and Invariance to Semantic and Lexical Alterations) benchmark, designed to evaluate the semantic and lexical understanding of language models. VISLA presents a 3-way semantic (in)equivalence task with a triplet of sentences associated with an image, to evaluate both vision-language models (VLMs) and unimodal language models (ULMs). An evaluation involving 34 VLMs and 20 ULMs reveals surprising difficulties in distinguishing between lexical and semantic variations. Spatial semantics encoded by language models also appear to be highly sensitive to lexical information. Notably, text encoders of VLMs demonstrate greater sensitivity to semantic and lexical variations than unimodal text encoders. Our contributions include the unification of image-to-text and text-to-text retrieval tasks, an off-the-shelf evaluation without fine-tuning, and assessing LMs' semantic (in)variance in the presence of lexical alterations. The results highlight strengths and weaknesses across diverse vision and unimodal language models, contributing to a deeper understanding of their capabilities. % VISLA enables a rigorous evaluation, shedding light on language models' capabilities in handling semantic and lexical nuances. Data and code will be made available at https://github.com/Sri-Harsha/visla_benchmark.

Related papers

• SUGARCREPE++ Dataset: Vision-Language Model Sensitivity to Semantic and Lexical Alterations [13.608653575298183]
  We introduce the SUGARCREPE++ dataset to analyze the sensitivity of vision-and-language models to semantic alterations. We show that all the models which achieve better performance on compositionality datasets need not perform equally well on SUGARCREPE++.
  arXiv  Detail & Related papers  (2024-06-17T03:22:20Z)
• CODIS: Benchmarking Context-Dependent Visual Comprehension for Multimodal Large Language Models [58.95889895912716]
  We introduce a new benchmark, named as CODIS, designed to assess the ability of models to use context provided in free-form text to enhance visual comprehension. Our findings indicate that MLLMs consistently fall short of human performance on this benchmark. This underscores the pressing need to enhance the ability of MLLMs to comprehend visuals in a context-dependent manner.
  arXiv  Detail & Related papers  (2024-02-21T08:21:12Z)
• Unified Language-Vision Pretraining in LLM with Dynamic Discrete Visual Tokenization [52.935150075484074]
  We introduce a well-designed visual tokenizer to translate the non-linguistic image into a sequence of discrete tokens like a foreign language. The resulting visual tokens encompass high-level semantics worthy of a word and also support dynamic sequence length varying from the image. This unification empowers LaVIT to serve as an impressive generalist interface to understand and generate multi-modal content simultaneously.
  arXiv  Detail & Related papers  (2023-09-09T03:01:38Z)
• ViLTA: Enhancing Vision-Language Pre-training through Textual Augmentation [35.05755930636518]
  We propose ViLTA, comprising of two components to further facilitate the model to learn fine-grained representations among image-text pairs. For Masked Language Modeling (MLM), we propose a cross-distillation method to generate soft labels to enhance the robustness of model. For Image-Text Matching (ITM), we leverage the current language encoder to synthesize hard negatives based on the context of language input.
  arXiv  Detail & Related papers  (2023-08-31T12:46:36Z)
• Can Linguistic Knowledge Improve Multimodal Alignment in Vision-Language Pretraining? [34.609984453754656]
  We aim to elucidate the impact of comprehensive linguistic knowledge, including semantic expression and syntactic structure, on multimodal alignment. Specifically, we design and release the SNARE, the first large-scale multimodal alignment probing benchmark.
  arXiv  Detail & Related papers  (2023-08-24T16:17:40Z)
• SenteCon: Leveraging Lexicons to Learn Human-Interpretable Language Representations [51.08119762844217]
  SenteCon is a method for introducing human interpretability in deep language representations. We show that SenteCon provides high-level interpretability at little to no cost to predictive performance on downstream tasks.
  arXiv  Detail & Related papers  (2023-05-24T05:06:28Z)
• Perceptual Grouping in Contrastive Vision-Language Models [59.1542019031645]
  We show how vision-language models are able to understand where objects reside within an image and group together visually related parts of the imagery. We propose a minimal set of modifications that results in models that uniquely learn both semantic and spatial information.
  arXiv  Detail & Related papers  (2022-10-18T17:01:35Z)
• Transition-based Abstract Meaning Representation Parsing with Contextual Embeddings [0.0]
  We study a way of combing two of the most successful routes to meaning of language--statistical language models and symbolic semantics formalisms--in the task of semantic parsing. We explore the utility of incorporating pretrained context-aware word embeddings--such as BERT and RoBERTa--in the problem of parsing.
  arXiv  Detail & Related papers  (2022-06-13T15:05:24Z)
• Multilingual Extraction and Categorization of Lexical Collocations with Graph-aware Transformers [86.64972552583941]
  We put forward a sequence tagging BERT-based model enhanced with a graph-aware transformer architecture, which we evaluate on the task of collocation recognition in context. Our results suggest that explicitly encoding syntactic dependencies in the model architecture is helpful, and provide insights on differences in collocation typification in English, Spanish and French.
  arXiv  Detail & Related papers  (2022-05-23T16:47:37Z)
• AM2iCo: Evaluating Word Meaning in Context across Low-ResourceLanguages with Adversarial Examples [51.048234591165155]
  We present AM2iCo, Adversarial and Multilingual Meaning in Context. It aims to faithfully assess the ability of state-of-the-art (SotA) representation models to understand the identity of word meaning in cross-lingual contexts. Results reveal that current SotA pretrained encoders substantially lag behind human performance.
  arXiv  Detail & Related papers  (2021-04-17T20:23:45Z)
• A Closer Look at Linguistic Knowledge in Masked Language Models: The Case of Relative Clauses in American English [17.993417004424078]
  Transformer-based language models achieve high performance on various tasks, but we still lack understanding of the kind of linguistic knowledge they learn and rely on. We evaluate three models (BERT, RoBERTa, and ALBERT) testing their grammatical and semantic knowledge by sentence-level probing, diagnostic cases, and masked prediction tasks.
  arXiv  Detail & Related papers  (2020-11-02T13:25:39Z)

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.