DALL-E 2 Fails to Reliably Capture Common Syntactic Processes

• URL: http://arxiv.org/abs/2210.12889v2
• Date: Tue, 25 Oct 2022 05:16:50 GMT
• Title: DALL-E 2 Fails to Reliably Capture Common Syntactic Processes
• Authors: Evelina Leivada, Elliot Murphy, Gary Marcus
• Abstract summary: We analyze the ability of DALL-E 2 to capture 8 grammatical phenomena pertaining to compositionality. We show that DALL-E 2 is unable to reliably infer meanings that are consistent with the syntax.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Machine intelligence is increasingly being linked to claims about sentience, language processing, and an ability to comprehend and transform natural language into a range of stimuli. We systematically analyze the ability of DALL-E 2 to capture 8 grammatical phenomena pertaining to compositionality that are widely discussed in linguistics and pervasive in human language: binding principles and coreference, passives, word order, coordination, comparatives, negation, ellipsis, and structural ambiguity. Whereas young children routinely master these phenomena, learning systematic mappings between syntax and semantics, DALL-E 2 is unable to reliably infer meanings that are consistent with the syntax. These results challenge recent claims concerning the capacity of such systems to understand of human language. We make available the full set of test materials as a benchmark for future testing.

Related papers

• Principles of semantic and functional efficiency in grammatical patterning [1.6267479602370545]
  Grammatical features such as number and gender serve two central functions in human languages. Number and gender encode salient semantic attributes like numerosity and animacy, but offload sentence processing cost by predictably linking words together. Grammars exhibit consistent organizational patterns across diverse languages, invariably rooted in a semantic foundation.
  arXiv  Detail & Related papers  (2024-10-21T10:49:54Z)
• Reframing linguistic bootstrapping as joint inference using visually-grounded grammar induction models [31.006803764376475]
  Semantic and syntactic bootstrapping posit that children use their prior knowledge of one linguistic domain, say syntactic relations, to help later acquire another, such as the meanings of new words. Here, we argue that they are instead both contingent on a more general learning strategy for language acquisition: joint learning. Using a series of neural visually-grounded grammar induction models, we demonstrate that both syntactic and semantic bootstrapping effects are strongest when syntax and semantics are learnt simultaneously.
  arXiv  Detail & Related papers  (2024-06-17T18:01:06Z)
• BabySLM: language-acquisition-friendly benchmark of self-supervised spoken language models [56.93604813379634]
  Self-supervised techniques for learning speech representations have been shown to develop linguistic competence from exposure to speech without the need for human labels. We propose a language-acquisition-friendly benchmark to probe spoken language models at the lexical and syntactic levels. We highlight two exciting challenges that need to be addressed for further progress: bridging the gap between text and speech and between clean speech and in-the-wild speech.
  arXiv  Detail & Related papers  (2023-06-02T12:54:38Z)
• We're Afraid Language Models Aren't Modeling Ambiguity [136.8068419824318]
  Managing ambiguity is a key part of human language understanding. We characterize ambiguity in a sentence by its effect on entailment relations with another sentence. We show that a multilabel NLI model can flag political claims in the wild that are misleading due to ambiguity.
  arXiv  Detail & Related papers  (2023-04-27T17:57:58Z)
• A Linguistic Investigation of Machine Learning based Contradiction Detection Models: An Empirical Analysis and Future Perspectives [0.34998703934432673]
  We analyze two Natural Language Inference data sets with respect to their linguistic features. The goal is to identify those syntactic and semantic properties that are particularly hard to comprehend for a machine learning model.
  arXiv  Detail & Related papers  (2022-10-19T10:06:03Z)
• Transparency Helps Reveal When Language Models Learn Meaning [71.96920839263457]
  Our systematic experiments with synthetic data reveal that, with languages where all expressions have context-independent denotations, both autoregressive and masked language models learn to emulate semantic relations between expressions. Turning to natural language, our experiments with a specific phenomenon -- referential opacity -- add to the growing body of evidence that current language models do not well-represent natural language semantics.
  arXiv  Detail & Related papers  (2022-10-14T02:35:19Z)
• AUTOLEX: An Automatic Framework for Linguistic Exploration [93.89709486642666]
  We propose an automatic framework that aims to ease linguists' discovery and extraction of concise descriptions of linguistic phenomena. Specifically, we apply this framework to extract descriptions for three phenomena: morphological agreement, case marking, and word order. We evaluate the descriptions with the help of language experts and propose a method for automated evaluation when human evaluation is infeasible.
  arXiv  Detail & Related papers  (2022-03-25T20:37:30Z)
• 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)
• Reinforcement learning of minimalist grammars [0.5862282909017474]
  State-of-the-art language technology scans the acoustically analyzed speech signal for relevant keywords. Words are then inserted into semantic slots to interpret the user's intent. A mental lexicon must be acquired by a cognitive agent during interaction with its users.
  arXiv  Detail & Related papers  (2020-04-30T14:25:58Z)
• Semantics-Aware Inferential Network for Natural Language Understanding [79.70497178043368]
  We propose a Semantics-Aware Inferential Network (SAIN) to meet such a motivation. Taking explicit contextualized semantics as a complementary input, the inferential module of SAIN enables a series of reasoning steps over semantic clues. Our model achieves significant improvement on 11 tasks including machine reading comprehension and natural language inference.
  arXiv  Detail & Related papers  (2020-04-28T07:24:43Z)

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.