How Language Models Prioritize Contextual Grammatical Cues?

• URL: http://arxiv.org/abs/2410.03447v1
• Date: Fri, 4 Oct 2024 14:09:05 GMT
• Title: How Language Models Prioritize Contextual Grammatical Cues?
• Authors: Hamidreza Amirzadeh, Afra Alishahi, Hosein Mohebbi,
• Abstract summary: We investigate how language models handle gender agreement when multiple gender cue words are present. Our findings reveal striking differences in how encoder-based and decoder-based models prioritize and use contextual information for their predictions.
• Score: 3.9790222241649587
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Transformer-based language models have shown an excellent ability to effectively capture and utilize contextual information. Although various analysis techniques have been used to quantify and trace the contribution of single contextual cues to a target task such as subject-verb agreement or coreference resolution, scenarios in which multiple relevant cues are available in the context remain underexplored. In this paper, we investigate how language models handle gender agreement when multiple gender cue words are present, each capable of independently disambiguating a target gender pronoun. We analyze two widely used Transformer-based models: BERT, an encoder-based, and GPT-2, a decoder-based model. Our analysis employs two complementary approaches: context mixing analysis, which tracks information flow within the model, and a variant of activation patching, which measures the impact of cues on the model's prediction. We find that BERT tends to prioritize the first cue in the context to form both the target word representations and the model's prediction, while GPT-2 relies more on the final cue. Our findings reveal striking differences in how encoder-based and decoder-based models prioritize and use contextual information for their predictions.

Related papers

• To token or not to token: A Comparative Study of Text Representations for Cross-Lingual Transfer [23.777874316083984]
  We propose a scoring Language Quotient metric capable of providing a weighted representation of both zero-shot and few-shot evaluation combined. Our analysis reveals that image-based models excel in cross-lingual transfer when languages are closely related and share visually similar scripts. In dependency parsing tasks where word relationships play a crucial role, models with their character-level focus, outperform others.
  arXiv  Detail & Related papers  (2023-10-12T06:59:10Z)
• Visual Comparison of Language Model Adaptation [55.92129223662381]
  adapters are lightweight alternatives for model adaptation. In this paper, we discuss several design and alternatives for interactive, comparative visual explanation methods. We show that, for instance, an adapter trained on the language debiasing task according to context-0 embeddings introduces a new type of bias.
  arXiv  Detail & Related papers  (2022-08-17T09:25:28Z)
• A Generative Language Model for Few-shot Aspect-Based Sentiment Analysis [90.24921443175514]
  We focus on aspect-based sentiment analysis, which involves extracting aspect term, category, and predicting their corresponding polarities. We propose to reformulate the extraction and prediction tasks into the sequence generation task, using a generative language model with unidirectional attention. Our approach outperforms the previous state-of-the-art (based on BERT) on average performance by a large margins in few-shot and full-shot settings.
  arXiv  Detail & Related papers  (2022-04-11T18:31:53Z)
• Towards Generalized Models for Task-oriented Dialogue Modeling on Spoken Conversations [22.894541507068933]
  This paper presents our approach to build generalized models for the Knowledge-grounded Task-oriented Dialogue Modeling on Spoken Conversations Challenge of DSTC-10. We employ extensive data augmentation strategies on written data, including artificial error injection and round-trip text-speech transformation. Our approach ranks third on the objective evaluation and second on the final official human evaluation.
  arXiv  Detail & Related papers  (2022-03-08T12:26:57Z)
• Exploring Multi-Modal Representations for Ambiguity Detection & Coreference Resolution in the SIMMC 2.0 Challenge [60.616313552585645]
  We present models for effective Ambiguity Detection and Coreference Resolution in Conversational AI. Specifically, we use TOD-BERT and LXMERT based models, compare them to a number of baselines and provide ablation experiments. Our results show that (1) language models are able to exploit correlations in the data to detect ambiguity; and (2) unimodal coreference resolution models can avoid the need for a vision component.
  arXiv  Detail & Related papers  (2022-02-25T12:10:02Z)
• A Latent-Variable Model for Intrinsic Probing [93.62808331764072]
  We propose a novel latent-variable formulation for constructing intrinsic probes. We find empirical evidence that pre-trained representations develop a cross-lingually entangled notion of morphosyntax.
  arXiv  Detail & Related papers  (2022-01-20T15:01:12Z)
• Leveraging recent advances in Pre-Trained Language Models forEye-Tracking Prediction [0.0]
  Natural Language Pro-cessing uses human-derived behavioral data like eye-tracking data to augment the neural nets to solve arange of tasks spanning syntax and semantics. In this paper,we use the ZuCo 1.0 and ZuCo 2.0 dataset to explore differ-ent linguistic models to directly predict thesegaze features for each word with respect to itssentence.
  arXiv  Detail & Related papers  (2021-10-09T06:46:48Z)
• Exemplar-Controllable Paraphrasing and Translation using Bitext [57.92051459102902]
  We adapt models from prior work to be able to learn solely from bilingual text (bitext) Our single proposed model can perform four tasks: controlled paraphrase generation in both languages and controlled machine translation in both language directions.
  arXiv  Detail & Related papers  (2020-10-12T17:02:50Z)
• SPLAT: Speech-Language Joint Pre-Training for Spoken Language Understanding [61.02342238771685]
  Spoken language understanding requires a model to analyze input acoustic signal to understand its linguistic content and make predictions. Various pre-training methods have been proposed to learn rich representations from large-scale unannotated speech and text. We propose a novel semi-supervised learning framework, SPLAT, to jointly pre-train the speech and language modules.
  arXiv  Detail & Related papers  (2020-10-05T19:29:49Z)
• Analysis of Predictive Coding Models for Phonemic Representation Learning in Small Datasets [0.0]
  The present study investigates the behaviour of two predictive coding models, Autoregressive Predictive Coding and Contrastive Predictive Coding, in a phoneme discrimination task. Our experiments show a strong correlation between the autoregressive loss and the phoneme discrimination scores with the two datasets. The CPC model shows rapid convergence already after one pass over the training data, and, on average, its representations outperform those of APC on both languages.
  arXiv  Detail & Related papers  (2020-07-08T15:46:13Z)

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.