What if This Modified That? Syntactic Interventions with Counterfactual Embeddings (2021.findings-acl)
Copied to clipboard
| Challenge: | Prior art aims to uncover meaningful properties within model representations, but it is unclear how faithfully such probes portray information that the models actually use. |
| Approach: | They propose a technique for generating counterfactual embeddings within models . they produce evidence that some models use a tree-distancelike representation of syntax . |
| Outcome: | The proposed technique produces evidence that some models use tree-distancelike representations of syntax in downstream prediction tasks. |
Similar Papers
Overestimation of Syntactic Representation in Neural Language Models (2020.acl-main)
Copied to clipboard
| Challenge: | Several testing methodologies have been developed to probe models’ syntactic representations. |
| Approach: | They propose a method to determine syntactic structure by training a model on strings generated according to a template and testing its ability to distinguish between similar ones with different syntax. |
| Outcome: | The proposed method reproduces positive results with two non-syntactic baseline language models: an n-gram model and an LSTM model trained on scrambled inputs. |
When Does Syntax Mediate Neural Language Model Performance? Evidence from Dropout Probes (2022.naacl-main)
Copied to clipboard
| Challenge: | Recent studies show that models encode syntactic information redundantly . this allows researchers to boost models' performance by injecting syntaktic information into embeddings . |
| Approach: | They propose a new probe design that guides probes to consider all syntactic information present in embeddings. |
| Outcome: | The proposed model improves performance by injecting syntactic information into models. |
Exploiting Syntactic Structure for Better Language Modeling: A Syntactic Distance Approach (2020.acl-main)
Copied to clipboard
| Challenge: | incorporating syntactic structure into language models has been a challenge since the 1990s. |
| Approach: | They propose to use syntactic information to integrate syntastic structure into neural language models by providing ground truth parse trees as additional training signals. |
| Outcome: | The proposed model achieves lower perplexity and better quality when ground truth parse trees are provided as training signals. |
Counterfactual reasoning: Testing language models’ understanding of hypothetical scenarios (2023.acl-short)
Copied to clipboard
| Challenge: | Existing language models have demonstrated remarkable improvements in downstream tasks, but it remains unclear to what extent they are consequences of correlation with linguistic heuristics versus robust reasoning about causal relations grounded on understanding of world knowledge. |
| Approach: | They propose to test language models with counterfactual conditionals to test their ability to distinguish hypothetical scenarios from reality. |
| Outcome: | The proposed model overrides real-world knowledge in counterfactual scenarios, but most models are driven by lexical cues. |
Generating Realistic Natural Language Counterfactuals (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods to explain ML tasks for natural language text are either unrealistic or introduce imperceptible changes. |
| Approach: | They propose a method that combines a conditional GAN and embeddings of a pretrained BERT encoder to model-agnostically generate realistic natural language text counterfactuals for explaining regression and classification tasks. |
| Outcome: | The proposed method outperforms baseline methods on fidelity and human judgments of naturalness across multiple datasets and multiple predictive models. |
Syntactic Data Augmentation Increases Robustness to Inference Heuristics (2020.acl-main)
Copied to clipboard
| Challenge: | Pretrained neural models lack sensitivity to word order on controlled challenge sets . augmentation methods that improve accuracy on standard training sets may be a problem . |
| Approach: | They propose to augment standard training sets with syntactically informative examples by applying syntastic transformations to sentences from the MNLI corpus. |
| Outcome: | The proposed method improved BERT’s accuracy on controlled examples that diagnose sensitivity to word order from 0.28 to 0.73 without affecting performance on the MNLI test set. |
How to represent a word and predict it, too: Improving tied architectures for language modelling (D18-1)
Copied to clipboard
| Challenge: | Recent state-of-the-art models use word embeddings as input and output mappings instead of tied models. |
| Approach: | They propose to decouple hidden state from word embedding prediction . they extend their proposed modification to word2vec models . |
| Outcome: | The proposed architectures achieve comparable or better results compared to previous models without tying . the proposed architecture reduces parameters, enabling more compact models and faster learning. |
Counterfactuals of Counterfactuals: a back-translation-inspired approach to analyse counterfactual editors (2023.findings-acl)
Copied to clipboard
George Filandrianos, Edmund Dervakos, Orfeas Menis Mastromichalakis, Chrysoula Zerva, Giorgos Stamou
| Challenge: | Existing explanations for classifiers are counterfactual or contrastive . lack of universal ground truth for counterf actual edits hinders their evaluation . |
| Approach: | They propose a back translation-inspired evaluation methodology that utilises earlier outputs of the explainer as ground truth proxies to investigate the consistency of explainers. |
| Outcome: | The proposed method can provide valuable insights into the behaviour of predictor and explainer models and infer patterns that would otherwise be obscured. |
Reasoning or Reciting? Exploring the Capabilities and Limitations of Language Models Through Counterfactual Tasks (2024.naacl-long)
Copied to clipboard
Zhaofeng Wu, Linlu Qiu, Alexis Ross, Ekin Akyürek, Boyuan Chen, Bailin Wang, Najoung Kim, Jacob Andreas, Yoon Kim
| Challenge: | Recent language models possess impressive performance across a wide range of tasks . however, they often rely on narrow, non-transferable procedures for task-solving . |
| Approach: | They propose to evaluate language models using "counterfactual" task variants that deviate from standard tasks. |
| Outcome: | The proposed framework shows that language models perform better on a wide range of tasks compared to the default conditions. |
Treble Counterfactual VLMs: A Causal Approach to Hallucination (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing studies link hallucination to data or representation biases, but their causal origins remain unclear. |
| Approach: | They propose a causal framework to analyze and mitigate hallucination in vision-language models by using counterfactual analysis to estimate the Natural Direct Effect (NDE) of each modality and their interaction. |
| Outcome: | The proposed framework significantly reduces hallucination while preserving task performance while retaining reliability. |