Papers with NLG) tasks
Automatic and Human-AI Interactive Text Generation (with a focus on Text Simplification and Revision) (2024.acl-tutorials)
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| Challenge: | In this tutorial, we focus on text-to-text generation, a class of natural language generation tasks, that takes a piece of text as input and then generates a revision that is improved according to some specific criteria. |
| Approach: | This tutorial focuses on text-to-text generation, a class of natural language generation tasks that takes a piece of text as input and generates a revision that is improved according to some specific criteria. |
| Outcome: | This tutorial focuses on text-to-text generation, a class of natural language generation tasks, that takes a piece of text as input and generates a revision that is improved according to some specificcriteria. |
Automatic Generation of Personalized Comment Based on User Profile (P19-2)
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| Challenge: | Experimental results show that our model can generate natural, human-like and personalized comments. |
| Approach: | They propose a model that takes user profile into account when generating comments on social media and integrates it with a gated memory. |
| Outcome: | The proposed model can generate natural, human-like and personalized comments on social media. |
Control-DAG: Constrained Decoding for Non-Autoregressive Directed Acyclic T5 using Weighted Finite State Automata (2024.naacl-short)
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| Challenge: | Existing non-autoregressive (NAR) models fail to generate specified entity names in up to 40% of responses and produce OOV errors. |
| Approach: | They propose a constrained decoding algorithm for Directed Acyclic T5 model which offers lexical, vocabulary and length control. |
| Outcome: | The proposed model significantly improves on Schema Guided Dialogue and DART datasets, establishing strong results for Task-Oriented Dialog and Data-to-Text NLG. |
NLoRA: Nyström-Initiated Low-Rank Adaptation for Large Language Models (2025.findings-emnlp)
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| Challenge: | Parameter-efficient fine-tuning is essential for adapting large language models (LLMs). However, LoRA suffers from slow convergence and some recent LoRA variants, such as PiSSA, rely on Singular Value Decomposition (SVD) for initialization. |
| Approach: | They propose to introduce a small intermediate matrix between the low-rank matrices (A) and (B) and propose NyströmLoRA (NLoRA) which leverages Nyström-based initialization for SLoRA to improve its effectiveness and efficiency. |
| Outcome: | The proposed approach improves on 5 natural language generation tasks and 8 natural language understanding tasks with minimal parameter overhead. |
SCRIPT: A Subcharacter Compositional Representation Injection Module for Korean Pre-Trained Language Models (2026.findings-acl)
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| Challenge: | Korean uses a featural writing system in which each character is composed of subcharacter units known as Jamo. |
| Approach: | They propose a model-agnostic module that injects subcharacter compositional knowledge into Korean language models. |
| Outcome: | a new module improves embeddings of Korean subwords with structural granularity . the module improve grammatical regularities and semantic cohesive variations . |
D.Va: Validate Your Demonstration First Before You Use It (2025.acl-long)
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| Challenge: | In-context learning (ICL) heavily relies on selecting effective demonstrations to achieve outputs that better align with the expected results. |
| Approach: | They propose a method which integrates a demonstration validation perspective into this field and integrates it into the learning paradigm. |
| Outcome: | The proposed method surpasses all retrieval-based in-context learning techniques across both natural language understanding (NLU) and natural language generation (NLG) tasks. |
Near-Negative Distinction: Giving a Second Life to Human Evaluation Datasets (2022.emnlp-main)
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| Challenge: | Existing methods for evaluating progress in natural language generation tasks are expensive, difficult to reproduce, and non-reusable. |
| Approach: | They propose a new automatic evaluation method for NLG called Near-Negative Distinction that repurposes prior human annotations into NND tests. |
| Outcome: | The proposed method achieves higher correlation with human judgments than standard NLG evaluation metrics. |
Coupling Context Modeling with Zero Pronoun Recovering for Document-Level Natural Language Generation (2021.emnlp-main)
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| Challenge: | ZP-annotated natural language generation (NLG) corpora are scarce in pro-drop languages . despite efforts to bridge the discrepancy between human and machine, zero pronouns still persist in pro -drop tasks. |
| Approach: | They propose a highly adaptive two-stage approach to couple context modeling with ZP recovering to mitigate the ZP problem in NLG tasks. |
| Outcome: | The proposed approach can improve translation, question answering, and summarization tasks. |
Systematic Task Exploration with LLMs: A Study in Citation Text Generation (2024.acl-long)
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| Challenge: | Large language models (LLMs) provide unprecedented flexibility in defining and executing complex, creative natural language generation tasks. |
| Approach: | They propose a framework that consists of input manipulation, reference data, and output measurement to explore citation text generation. |
| Outcome: | The proposed framework explores citation text generation, a popular scholarly NLP task that lacks consensus on the task definition and evaluation metric and has not yet been tackled within the LLM paradigm. |
Jailbreak Open-Sourced Large Language Models via Enforced Decoding (2024.acl-long)
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Hangfan Zhang, Zhimeng Guo, Huaisheng Zhu, Bochuan Cao, Lu Lin, Jinyuan Jia, Jinghui Chen, Dinghao Wu
| Challenge: | Existing studies show that Large Language Models can be misused to generate undesired content. |
| Approach: | They propose to use large language models to manipulate the generation process to generate undesired content without heavy computations or prompt designs. |
| Outcome: | The proposed method shows that open-sourced large language models could be misused to generate undesired content without heavy computations or prompt designs. |
CodePrompt: Task-Agnostic Prefix Tuning for Program and Language Generation (2023.findings-acl)
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| Challenge: | Prompt-tuning methods have been used to solve inefficient parameter update and storage issues in Natural Language Generation tasks. |
| Approach: | They propose a task-agnostic prompt tuning method that reflects the traits of PLM for program language. |
| Outcome: | The proposed method is effective in three PLG tasks, not only in the full-data setting but also in the low-resource setting and cross-domain setting. |
Finding Memo: Extractive Memorization in Constrained Sequence Generation Tasks (2022.findings-emnlp)
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| Challenge: | Memorization presents a challenge for constrained Natural Language Generation tasks . previous studies focused on counterfactual memorization, linking it to hallucinations . |
| Approach: | They propose an algorithm for extractive memorization in constrained sequence generation tasks . they propose to elicit non-memorized translations of memorized samples from the same model . |
| Outcome: | The proposed algorithm could be leveraged to mitigate memorization in the model through finetuning. |
ConU: Conformal Uncertainty in Large Language Models with Correctness Coverage Guarantees (2024.findings-emnlp)
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Zhiyuan Wang, Jinhao Duan, Lu Cheng, Yue Zhang, Qingni Wang, Xiaoshuang Shi, Kaidi Xu, Heng Tao Shen, Xiaofeng Zhu
| Challenge: | Uncertainty quantification (UQ) in natural language generation tasks remains an open challenge . however, black-box uncertainty measures require investigating with the proliferation of LLMs served via APIs. |
| Approach: | They propose a conformal uncertainty measure and a method to transform heuristic uncertainty notions into rigorous prediction sets. |
| Outcome: | Empirical results show that the proposed method outperforms state-of-the-art methods and can provide reliable guarantees for open-ended NLG tasks. |
SEEval: Advancing LLM Text Evaluation Efficiency and Accuracy through Self-Explanation Prompting (2025.findings-naacl)
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| Challenge: | Large language models (LLMs) have achieved remarkable success in various natural language generation tasks, but their performance in automatic text evaluation is not ready as human replacements. |
| Approach: | They propose a prompt-based text evaluator that incorporates self-explanation, a metacognitive strategy, to enhance automatic text evaluation. |
| Outcome: | The proposed method achieves competitive and often superior performance compared to the two state-of-the-art baselines – G-Eval and Analyze-Rate – and is 20 times more efficient in terms of run-time. |
COSMMIC: Comment-Sensitive Multimodal Multilingual Indian Corpus for Summarization and Headline Generation (2025.acl-long)
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Raghvendra Kumar, Mohammed Salman S A, Aryan Sahu, Tridib Nandi, Pragathi Y P, Sriparna Saha, Jose G Moreno
| Challenge: | COSMMIC is a multimodal, multilingual dataset featuring nine major Indian languages. |
| Approach: | They propose a multimodal, multilingual multimodal multimodal dataset that integrates text, images and user feedback to enhance summarization. |
| Outcome: | The proposed dataset is based on 4,959 article-image pairs and 24,484 reader comments with ground-truth summaries available in all included languages. |
Dual Inference for Improving Language Understanding and Generation (2020.findings-emnlp)
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| Challenge: | Existing studies have exploited the duality of the task pairs in machine translation and speech recognition. |
| Approach: | They propose to leverage the duality in the inference stage without retraining whole models. |
| Outcome: | The proposed method is effective in both NLU and NLG tasks, providing the great potential of practical use. |
Bridging Subword Gaps in Pretrain-Finetune Paradigm for Natural Language Generation (2021.acl-long)
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| Challenge: | Existing methods to pretrain language models are limited by one-size-fits-all vocabulary . embeddings of mismatch tokens can be efficiently initialized in downstream tasks . |
| Approach: | They propose to extend pretrain-finetune pipeline with an embedding transfer step . plug-and-play embeddable generator is introduced to generate any input token . |
| Outcome: | The proposed approach allows for more efficient and better performed NLG models. |
MVP: Multi-task Supervised Pre-training for Natural Language Generation (2023.findings-acl)
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| Challenge: | Pre-trained language models (PLMs) have achieved remarkable success in natural language generation tasks. |
| Approach: | They propose to use a large-scale natural language generation corpus to pre-train a text generation model MVP in a supervised manner. |
| Outcome: | The proposed model outperforms BART and Flan-T5 on 13 out of 17 datasets and outperformed BART by 9.3% and FlaN-T5. |
imapScore: Medical Fact Evaluation Made Easy (2024.findings-acl)
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| Challenge: | Automated evaluation of natural language generation tasks fails to focus on medical QA because of the diversity in medical terminology. |
| Approach: | They propose a new data structure, imap, to capture key information in questions and answers. |
| Outcome: | The proposed model outperforms state-of-the-art metrics in correlation with human scores. |
Contrastive Error Attribution for Finetuned Language Models (2023.acl-long)
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| Challenge: | Existing methods for error tracing do not detect faithfulness errors in NLG datasets. |
| Approach: | They propose a framework to identify and remove low-quality training instances that lead to undesirable outputs. |
| Outcome: | The proposed method outperforms existing methods for detecting faithfulness errors in NLG datasets. |
FOCUS: Forging Originality through Contrastive Use in Self-Plagiarism for Language Models (2024.findings-acl)
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| Challenge: | Existing methods to generate original text using pre-trained language models are problematic as they are trained on corpora constructed by human authors. |
| Approach: | They propose a unique “self-plagiarism” contrastive decoding strategy that modifies prompts in LLMs to develop an amateur model and a professional model. |
| Outcome: | The proposed method enables the development of an amateur model and a professional model while maintaining its standard language model status. |
What Comes Next? Evaluating Uncertainty in Neural Text Generators Against Human Production Variability (2023.emnlp-main)
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| Challenge: | In Natural Language Generation tasks, multiple communicative goals are plausible and any goal can be put into words, or produced, in multiple ways. |
| Approach: | They characterise the extent to which human production varies lexically, syntactically, and semantically across four NLG tasks, connecting human production variability to aleatoric or data uncertainty. |
| Outcome: | The proposed model can be calibrated to human production variability using multiple samples and, when possible, multiple references. |
Themis: A Reference-free NLG Evaluation Language Model with Flexibility and Interpretability (2024.emnlp-main)
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| Challenge: | Existing methods for evaluation of natural language generation tasks lack reliable data. |
| Approach: | They propose to use annotations from human and GPT-4 to construct a corpus for NLG evaluation. |
| Outcome: | The proposed corpus can perform flexible and interpretable evaluations without references and surpasses existing models. |
Astra: Activation-Space Tail-Eigenvector Low-Rank Adaptation of Large Language Models (2026.findings-acl)
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| Challenge: | Existing methods for fine-tuning pre-trained models are limited due to suboptimal activation subspaces. |
| Approach: | They propose a method that leverages tail eigenvectors of model output activations to construct low-rank adapters. |
| Outcome: | The proposed method outperforms existing methods across 16 benchmarks and surpasses full fine-tuning in certain scenarios. |
Argument Summarization and its Evaluation in the Era of Large Language Models (2025.emnlp-main)
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Moritz Altemeyer, Steffen Eger, Johannes Daxenberger, Yanran Chen, Tim Altendorf, Philipp Cimiano, Benjamin Schiller
| Challenge: | Large Language Models (LLMs) have revolutionized various Natural Language Generation tasks, including Argument Summarization (ArgSum). |
| Approach: | They propose a prompt-based evaluation scheme and validate it through a human benchmark dataset. |
| Outcome: | The proposed evaluation scheme outperforms existing methods and is validated by a human benchmark dataset. |