Multi-Scale Distribution Deep Variational Autoencoder for Explanation Generation (2022.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for generating explanations for recommender systems produce generic explanations that fail to incorporate user and item specific details. |
| Approach: | They propose a multi-scale distribution deepvariational autoencoder with a prior network that eliminates noise while retaining meaningful signals in the input. |
| Outcome: | The proposed models can generate explanations with concrete input-specific contents. |
Similar Papers
Multi-Level Explanations for Generative Language Models (2025.acl-long)
Copied to clipboard
Lucas Monteiro Paes, Dennis Wei, Hyo Jin Do, Hendrik Strobelt, Ronny Luss, Amit Dhurandhar, Manish Nagireddy, Karthikeyan Natesan Ramamurthy, Prasanna Sattigeri, Werner Geyer, Soumya Ghosh
| Challenge: | Large language models (LLMs) are being used for context-grounded tasks like summarizing meetings and answering doctors' questions. |
| Approach: | They propose a technique to provide explanations for context-grounded text generation by assigning scores to parts of the context to quantify their influence on the model output. |
| Outcome: | The proposed framework can provide more faithful explanations of generated output than available alternatives, including LLM self-explanations. |
Scale-VAE: Preventing Posterior Collapse in Variational Autoencoder (2024.lrec-main)
Copied to clipboard
| Challenge: | Variational autoencoder (VAE) is a widely used generative model . but when employing strong autoregressive generation networks, VAE tends to converge to a degenerate local optimum known as posterior collapse. |
| Approach: | They propose a model called Scale-VAE to solve a posterior collapse problem . they use a factor to keep the posterior dimension discriminative across data instances . |
| Outcome: | The proposed model outperforms state-of-the-art models in density estimation and representation learning. |
On the Importance of the Kullback-Leibler Divergence Term in Variational Autoencoders for Text Generation (D19-56)
Copied to clipboard
| Challenge: | Variational Autoencoders suffer from learning uninformative latent representations due to issues such as approximated posterior collapse or entanglement of the latent space. |
| Approach: | They propose to impose an explicit constraint on the Kullback-Leibler divergence term inside the VAE objective function to understand the significance of the KL term in controlling the information transmitted through the VAe channel. |
| Outcome: | The proposed constraint avoids posterior collapse, but it also controls the information transmitted through the VAE channel. |
Explicit Bayesian Inference to Uncover the Latent Themes of Large Language Models (2025.findings-acl)
Copied to clipboard
| Challenge: | Large language models (LLMs) have impressive generative capabilities, yet their inner mechanisms remain largely opaque. |
| Approach: | They propose a variational autoencoder-based neural topic model to interpret LLMs generation process through an explicit Bayesian framework by inferring latent topic variables via variational inference. |
| Outcome: | The proposed model outperforms state-of-the-art topic models on intrinsic measures of coherence and diversity on multiple datasets and shows significant gains on classification and summarization tasks. |
Variational Inference and Deep Generative Models (P18-5)
Copied to clipboard
| Challenge: | Unsupervised and semi-supervised learning has been addressed scarcely in NLP . this tutorial provides an introduction to variational inference followed by an example-driven discussion of how to use variational methods for training DGMs. |
| Approach: | This tutorial provides an introduction to variational inference followed by an example-driven discussion of how to use variational methods for training DGMs. |
| Outcome: | This tutorial provides an introduction to variational inference followed by an example-driven discussion of how to use variational methods for training DGMs. |
VCDM: Leveraging Variational Bi-encoding and Deep Contextualized Word Representations for Improved Definition Modeling (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches for definition modeling combine distributional and lexical semantics in an implicit rather than direct way. |
| Approach: | They propose a model that introduces a continuous latent variable to model the relationship between a phrase and its definition. |
| Outcome: | The proposed model achieves state-of-the-art performance on four challenging benchmarks and the first non-English corpus. |
A Hierarchical Latent Structure for Variational Conversation Modeling (N18-1)
Copied to clipboard
| Challenge: | Variational autoencoders suffer from the notorious degeneration problem, according to a new study . utterance drop regularization is an important feature of the hierarchical RNNs . |
| Approach: | They propose a variational hierarchical conversation RNN framework that exploits latent variables and an utterance drop regularization to exploit latent variable. |
| Outcome: | The proposed model outperforms state-of-the-art models on Cornell Movie Dialog and Ubuntu Dialog Corpus. |
Effective Estimation of Deep Generative Language Models (2020.acl-main)
Copied to clipboard
| Challenge: | Existing techniques for parameterisation of probabilistic models by deep neural networks are difficult to use in language modelling due to posterior collapse. |
| Approach: | They propose to use variational auto-encoder to estimate probabilistic models of language by deep neural networks. |
| Outcome: | The proposed model performs reasonably well given enough resources, but a favourite can be named based on convenience. |
Implicit Deep Latent Variable Models for Text Generation (D19-1)
Copied to clipboard
| Challenge: | Variational auto-encoders have been used for text generation but their representation power is limited due to two reasons. |
| Approach: | They advocate sample-based representations of variational distributions for natural language . they further develop an LVM to directly match the aggregated posterior to the prior . |
| Outcome: | The proposed model can be viewed as a natural extension of VAEs with a regularization of maximizing mutual information, mitigating the "posterior collapse" issue. |
Generating Hierarchical Explanations on Text Classification via Feature Interaction Detection (2020.acl-main)
Copied to clipboard
| Challenge: | Existing methods for generating explanations for neural networks ignore feature interactions between words and phrases. |
| Approach: | They propose to build hierarchical explanations by detecting feature interactions by combining words and phrases at different levels of the hierarchy. |
| Outcome: | The proposed method is evaluated on two benchmark datasets, via automatic and human evaluations. |