Papers with classifier guidance

2 papers
SSD-LM: Semi-autoregressive Simplex-based Diffusion Language Model for Text Generation and Modular Control (2023.acl-long)

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Challenge: Existing diffusion models for continuous-valued domains have not been adopted for text data.
Approach: They propose a diffusion-based language model with two key design choices . semi-autoregressive model generates blocks of text and allows local context updates . they evaluate it on unconstrained text generation benchmarks .
Outcome: The proposed model outperforms autoregressive models on unconstrained text generation benchmarks on uncontrolled text generation.
Enhancing AI Assisted Writing with One-Shot Implicit Negative Feedback (2024.emnlp-main)

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Challenge: Various systems have been proposed to draft and automate replies for users . yet, the heterogeneity of the inputs and architectures often renders it difficult to combine insights from user behaviour in one system to improve performance in another.
Approach: They propose an approach that uses classifier guidance to controllably integrate implicit user feedback into the text generation process.
Outcome: The proposed approach improves Rouge-L, generating the correct intent and generating an 86% win-rate on the multiWOZ and Schema-Guided Dialog datasets.

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