Counterfactual Generation from Language Models

• URL: http://arxiv.org/abs/2411.07180v2
• Date: Sat, 23 Nov 2024 11:00:34 GMT
• Title: Counterfactual Generation from Language Models
• Authors: Shauli Ravfogel, Anej Svete, Vésteinn Snæbjarnarson, Ryan Cotterell,
• Abstract summary: We show that counterfactual reasoning is conceptually distinct from interventions. We propose a framework for generating true string counterfactuals. Our experiments demonstrate that the approach produces meaningful counterfactuals.
• Score: 64.55296662926919
• License:
• Abstract: Understanding and manipulating the causal generation mechanisms in language models is essential for controlling their behavior. Previous work has primarily relied on techniques such as representation surgery -- e.g., model ablations or manipulation of linear subspaces tied to specific concepts -- to intervene on these models. To understand the impact of interventions precisely, it is useful to examine counterfactuals -- e.g., how a given sentence would have appeared had it been generated by the model following a specific intervention. We highlight that counterfactual reasoning is conceptually distinct from interventions, as articulated in Pearl's causal hierarchy. Based on this observation, we propose a framework for generating true string counterfactuals by reformulating language models as Generalized Structural-equation. Models using the Gumbel-max trick. This allows us to model the joint distribution over original strings and their counterfactuals resulting from the same instantiation of the sampling noise. We develop an algorithm based on hindsight Gumbel sampling that allows us to infer the latent noise variables and generate counterfactuals of observed strings. Our experiments demonstrate that the approach produces meaningful counterfactuals while at the same time showing that commonly used intervention techniques have considerable undesired side effects.

Related papers

• Counterfactual Generative Modeling with Variational Causal Inference [1.9287470458589586]
  We present a novel variational Bayesian causal inference framework to handle counterfactual generative modeling tasks. In experiments, we demonstrate the advantage of our framework compared to state-of-the-art models in counterfactual generative modeling.
  arXiv  Detail & Related papers  (2024-10-16T16:44:12Z)
• Representation Surgery: Theory and Practice of Affine Steering [72.61363182652853]
  Language models often exhibit undesirable behavior, e.g., generating toxic or gender-biased text. One natural (and common) approach to prevent the model from exhibiting undesirable behavior is to steer the model's representations. This paper investigates the formal and empirical properties of steering functions.
  arXiv  Detail & Related papers  (2024-02-15T00:20:30Z)
• Nonparametric Partial Disentanglement via Mechanism Sparsity: Sparse Actions, Interventions and Sparse Temporal Dependencies [58.179981892921056]
  This work introduces a novel principle for disentanglement we call mechanism sparsity regularization. We propose a representation learning method that induces disentanglement by simultaneously learning the latent factors. We show that the latent factors can be recovered by regularizing the learned causal graph to be sparse.
  arXiv  Detail & Related papers  (2024-01-10T02:38:21Z)
• Diffusion Action Segmentation [63.061058214427085]
  We propose a novel framework via denoising diffusion models, which shares the same inherent spirit of such iterative refinement. In this framework, action predictions are iteratively generated from random noise with input video features as conditions.
  arXiv  Detail & Related papers  (2023-03-31T10:53:24Z)
• Probing for Incremental Parse States in Autoregressive Language Models [9.166953511173903]
  Next-word predictions from autoregressive neural language models show remarkable sensitivity to syntax. This work evaluates the extent to which this behavior arises as a result of a learned ability to maintain implicit representations of incremental syntactic structures.
  arXiv  Detail & Related papers  (2022-11-17T18:15:31Z)
• NaturalAdversaries: Can Naturalistic Adversaries Be as Effective as Artificial Adversaries? [61.58261351116679]
  We introduce a two-stage adversarial example generation framework (NaturalAdversaries) for natural language understanding tasks. It is adaptable to both black-box and white-box adversarial attacks based on the level of access to the model parameters. Our results indicate these adversaries generalize across domains, and offer insights for future research on improving robustness of neural text classification models.
  arXiv  Detail & Related papers  (2022-11-08T16:37:34Z)
• Avoiding Inference Heuristics in Few-shot Prompt-based Finetuning [57.4036085386653]
  We show that prompt-based models for sentence pair classification tasks still suffer from a common pitfall of adopting inferences based on lexical overlap. We then show that adding a regularization that preserves pretraining weights is effective in mitigating this destructive tendency of few-shot finetuning.
  arXiv  Detail & Related papers  (2021-09-09T10:10:29Z)
• A Discrete Variational Recurrent Topic Model without the Reparametrization Trick [16.54912614895861]
  We show how to learn a neural topic model with discrete random variables. We show improved perplexity and document understanding across multiple corpora.
  arXiv  Detail & Related papers  (2020-10-22T20:53:44Z)
• Natural Language Inference with Mixed Effects [4.560556461930812]
  We propose a generic method that allows one to skip the aggregation step and train on the raw annotations directly without subjecting the model to unwanted noise. We demonstrate that this method, which generalizes the notion of a textitmixed effects model by incorporating textitannotator random effects into any existing neural model, improves performance over models that do not incorporate such effects.
  arXiv  Detail & Related papers  (2020-10-20T17:54:16Z)
• CausaLM: Causal Model Explanation Through Counterfactual Language Models [33.29636213961804]
  CausaLM is a framework for producing causal model explanations using counterfactual language representation models. We show that language representation models such as BERT can effectively learn a counterfactual representation for a given concept of interest. A byproduct of our method is a language representation model that is unaffected by the tested concept.
  arXiv  Detail & Related papers  (2020-05-27T15:06:35Z)

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.