Underspecification in Language Modeling Tasks: A Causality-Informed Study of Gendered Pronoun Resolution

• URL: http://arxiv.org/abs/2210.00131v4
• Date: Thu, 22 Feb 2024 18:52:15 GMT
• Title: Underspecification in Language Modeling Tasks: A Causality-Informed Study of Gendered Pronoun Resolution
• Authors: Emily McMilin
• Abstract summary: We introduce a simple causal mechanism to describe the role underspecification plays in the generation of spurious correlations. Despite its simplicity, our causal model directly informs the development of two lightweight black-box evaluation methods.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modern language modeling tasks are often underspecified: for a given token prediction, many words may satisfy the user's intent of producing natural language at inference time, however only one word will minimize the task's loss function at training time. We introduce a simple causal mechanism to describe the role underspecification plays in the generation of spurious correlations. Despite its simplicity, our causal model directly informs the development of two lightweight black-box evaluation methods, that we apply to gendered pronoun resolution tasks on a wide range of LLMs to 1) aid in the detection of inference-time task underspecification by exploiting 2) previously unreported gender vs. time and gender vs. location spurious correlations on LLMs with a range of A) sizes: from BERT-base to GPT-4 Turbo Preview, B) pre-training objectives: from masked & autoregressive language modeling to a mixture of these objectives, and C) training stages: from pre-training only to reinforcement learning from human feedback (RLHF). Code and open-source demos available at https://github.com/2dot71mily/uspec.

Related papers

• Pre-Trained Language-Meaning Models for Multilingual Parsing and Generation [14.309869321407522]
  We introduce multilingual pre-trained language-meaning models based on Discourse Representation Structures (DRSs) Since DRSs are language neutral, cross-lingual transfer learning is adopted to further improve the performance of non-English tasks. automatic evaluation results show that our approach achieves the best performance on both the multilingual DRS parsing and DRS-to-text generation tasks.
  arXiv  Detail & Related papers  (2023-05-31T19:00:33Z)
• Language Models Implement Simple Word2Vec-style Vector Arithmetic [32.2976613483151]
  A primary criticism towards language models (LMs) is their inscrutability. This paper presents evidence that, despite their size and complexity, LMs sometimes exploit a simple vector arithmetic style mechanism to solve some relational tasks.
  arXiv  Detail & Related papers  (2023-05-25T15:04:01Z)
• Interpretable Unified Language Checking [42.816372695828306]
  We present an interpretable, unified, language checking (UniLC) method for both human and machine-generated language. We find that LLMs can achieve high performance on a combination of fact-checking, stereotype detection, and hate speech detection tasks.
  arXiv  Detail & Related papers  (2023-04-07T16:47:49Z)
• Language Model Pre-Training with Sparse Latent Typing [66.75786739499604]
  We propose a new pre-training objective, Sparse Latent Typing, which enables the model to sparsely extract sentence-level keywords with diverse latent types. Experimental results show that our model is able to learn interpretable latent type categories in a self-supervised manner without using any external knowledge.
  arXiv  Detail & Related papers  (2022-10-23T00:37:08Z)
• Bridging the Gap between Language Models and Cross-Lingual Sequence Labeling [101.74165219364264]
  Large-scale cross-lingual pre-trained language models (xPLMs) have shown effectiveness in cross-lingual sequence labeling tasks. Despite the great success, we draw an empirical observation that there is a training objective gap between pre-training and fine-tuning stages. In this paper, we first design a pre-training task tailored for xSL named Cross-lingual Language Informative Span Masking (CLISM) to eliminate the objective gap. Second, we present ContrAstive-Consistency Regularization (CACR), which utilizes contrastive learning to encourage the consistency between representations of input parallel
  arXiv  Detail & Related papers  (2022-04-11T15:55:20Z)
• AdaPrompt: Adaptive Model Training for Prompt-based NLP [77.12071707955889]
  We propose AdaPrompt, adaptively retrieving external data for continual pretraining of PLMs. Experimental results on five NLP benchmarks show that AdaPrompt can improve over standard PLMs in few-shot settings. In zero-shot settings, our method outperforms standard prompt-based methods by up to 26.35% relative error reduction.
  arXiv  Detail & Related papers  (2022-02-10T04:04:57Z)
• Towards Language Modelling in the Speech Domain Using Sub-word Linguistic Units [56.52704348773307]
  We propose a novel LSTM-based generative speech LM based on linguistic units including syllables and phonemes. With a limited dataset, orders of magnitude smaller than that required by contemporary generative models, our model closely approximates babbling speech. We show the effect of training with auxiliary text LMs, multitask learning objectives, and auxiliary articulatory features.
  arXiv  Detail & Related papers  (2021-10-31T22:48:30Z)
• Masked Language Modeling and the Distributional Hypothesis: Order Word Matters Pre-training for Little [74.49773960145681]
  A possible explanation for the impressive performance of masked language model (MLM)-training is that such models have learned to represent the syntactic structures prevalent in NLP pipelines. In this paper, we propose a different explanation: pre-trains succeed on downstream tasks almost entirely due to their ability to model higher-order word co-occurrence statistics. Our results show that purely distributional information largely explains the success of pre-training, and underscore the importance of curating challenging evaluation datasets that require deeper linguistic knowledge.
  arXiv  Detail & Related papers  (2021-04-14T06:30:36Z)
• Task-specific Objectives of Pre-trained Language Models for Dialogue Adaptation [79.0866650271659]
  Common process of utilizing PrLMs is first pre-training on large-scale general corpora with task-independent LM training objectives, then fine-tuning on task datasets with task-specific training objectives. We introduce task-specific pre-training on in-domain task-related corpora with task-specific objectives. This procedure is placed between the original two stages to enhance the model understanding capacity of specific tasks.
  arXiv  Detail & Related papers  (2020-09-10T16:46:46Z)

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.