Papers with text generation approaches
Explaining Toxic Text via Knowledge Enhanced Text Generation (2022.naacl-main)
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| Challenge: | Existing work on toxic speech classification relies on generic and repetitive explanations . elucidating toxic speech can help with downstream tasks such as debiasing . |
| Approach: | They propose a knowledge-informed encoder-decoder framework to generate toxic text explanations . they use multiple knowledge sources to generate detailed explanations of toxic text . |
| Outcome: | The proposed model outperforms state-of-the-art models significantly in generating toxic explanations . the proposed model can generate detailed explanations of toxic speech compared to baselines compared with baseline models . |
Evidence-Driven Retrieval Augmented Response Generation for Online Misinformation (2024.naacl-long)
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| Challenge: | Existing methods to generate counter-misinformation responses are often trained end-to-end without external knowledge, resulting in subpar text quality and excessively repetitive responses. |
| Approach: | They propose retrieval augmented response generation for online misinformation (RARG) that collects supporting evidence and generates counter-misinformation responses via reinforcement learning from human feedback. |
| Outcome: | The proposed method outperforms baselines with extensive experiments with in- and cross-domain datasets and consistently generates high-quality counter-misinformation responses. |
Think More, Hallucinate Less: Mitigating Hallucinations via Dual Process of Fast and Slow Thinking (2025.findings-acl)
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| Challenge: | Existing studies have focused on the issue of hallucination in large language models. |
| Approach: | They propose a framework that allows an explicit slow thinking generation process for mitigating hallucinations during inference. |
| Outcome: | The proposed framework outperforms baseline approaches on English and Chinese datasets. |
Language Detoxification with Attribute-Discriminative Latent Space (2023.acl-long)
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| Challenge: | Existing methods to detoxify toxic text require excessive memory, computations and time. |
| Approach: | They propose a method to generate toxic text using an attribute-discriminative latent space. |
| Outcome: | The proposed method outperforms baselines on detoxified language and dialogue generation tasks while being time- and memory-efficient. |
Generating Summaries with Controllable Readability Levels (2023.emnlp-main)
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| Challenge: | Current text generation approaches focus on a specific readability level, resulting in texts that are not customized to readers’ proficiency levels. |
| Approach: | They propose to generate summaries with fine-grained control over their readability by using instruction-based readability control, reinforcement learning and lookahead to estimate readability of upcoming decoding steps. |
| Outcome: | The generated summaries with different readability levels were compared with previous methods that focus on a specific readability level (e.g., lay summarization) and a lookahead approach significantly improved readability control on news summarizing. |
TinyStyler: Efficient Few-Shot Text Style Transfer with Authorship Embeddings (2024.findings-emnlp)
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| Challenge: | Existing methods for text style transfer rely on few-shot capabilities of large language models or complex controllable text generation approaches that are inefficient and underperform on fluency metrics. |
| Approach: | They propose a lightweight but effective approach which leverages a small language model and pre-trained authorship embeddings to perform efficient, few-shot text style transfer. |
| Outcome: | The proposed method outperforms strong approaches such as GPT-4 and performs form attribute style transfer with automatic and human evaluations. |