Papers with controllability
What, When, and How to Ground: Designing User Persona-Aware Conversational Agents for Engaging Dialogue (2023.acl-industry)
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| Challenge: | a personalized dialogue system can generate user-customized responses based on long-term memory about the user's persona. |
| Approach: | They propose a method for building a personalized open-domain dialogue system . they combine weighted dataset blending and negative persona information augmentation methods . |
| Outcome: | The proposed method balances dialogue fluency and tendency to ground while introducing a response-type label to improve controllability and explainability of the grounded responses. |
ORAL: Prompting Your Large-Scale LoRAs via Conditional Recurrent Diffusion (2025.findings-emnlp)
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| Challenge: | Existing approaches to low-rank Adaptation (LoRA) are limited in scalability and controllability. |
| Approach: | They propose a conditional recurrent diffusion framework that generates LoRA parameters directly . they integrate model architecture and textual task specifications to generate task-specific parameters . |
| Outcome: | The proposed framework scales to billions-of-parameter LLMs and maintains controllability. |
GRS: Combining Generation and Revision in Unsupervised Sentence Simplification (2022.findings-acl)
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| Challenge: | Existing methods for sentence simplification are supervised or unsupervised . paraphrasing captures complex edit operations, while revision-based methods provide more control and interpretability. |
| Approach: | They propose an unsupervised approach to sentence simplification that combines text generation and text revision. |
| Outcome: | The proposed method improves on the Newsela and ASSET datasets. |
Large Language Models with Controllable Working Memory (2023.findings-acl)
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Daliang Li, Ankit Singh Rawat, Manzil Zaheer, Xin Wang, Michal Lukasik, Andreas Veit, Felix Yu, Sanjiv Kumar
| Challenge: | Large language models (LLMs) have led to a series of breakthroughs in natural language processing due to the massive amounts of world knowledge they memorize during pretraining. |
| Approach: | They propose a method to inject counterfactual and irrelevant contexts into standard supervised datasets to strengthen both controllability and robustness. |
| Outcome: | The proposed method improves controllability and robustness across model architectures and sizes. |
Diagnose, Then Repair: A Two-Stage MQM-Guided Post-Editing Framework for Domain-Specific Machine Translation (2026.acl-industry)
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| Challenge: | In practice, LLMs are largely diagnostic, with the signals rarely translating into direct quality improvements under real production constraints. |
| Approach: | They propose a two-stage, evaluator-guided automatic post-editing framework that turns MQM-style evaluation into targeted repairs. |
| Outcome: | The proposed framework improves both COMET and CometKiwi scores over one-stage evaluation methods while severities and error spans show strong agreement with human annotations and human editor preferences. |
Control Image Captioning Spatially and Temporally (2021.acl-long)
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| Challenge: | Existing methods to generate image captions with user intention are still under exploration. |
| Approach: | They propose a model that connects Contrastive constraints and Attention Guidance in a loop manner and engages explicit spatial and temporal constraints to the generating process. |
| Outcome: | The proposed model improves performance on a trace-controlled image captioning task. |
DiffusEmp: A Diffusion Model-Based Framework with Multi-Grained Control for Empathetic Response Generation (2023.acl-long)
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| Challenge: | Existing methods to generate empathetic responses are monotonous and generic, resulting in shallow empathy and few connections to the context. |
| Approach: | They propose to use explicit control to guide the empathy expression and a framework DiffusEmp to unify the utilization of dialogue context and attribute-oriented control signals. |
| Outcome: | The proposed framework outperforms baselines on EmpatheticDialogue in terms of controllability, informativeness, diversity, and diversity without the loss of context-relatedness. |
Detoxifying Language Models Risks Marginalizing Minority Voices (2021.naacl-main)
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| Challenge: | Existing detoxification techniques have been proposed to mitigate toxic LM generations . e.g., detoxification makes LMs more brittle to distribution shift, especially on language used by marginalized groups . |
| Approach: | They propose to use detoxification techniques to reduce toxic LM generations without affecting perplexity or generation quality on nontoxic inputs. |
| Outcome: | The proposed methods hurt equity on language used by marginalized groups, the authors show . they show that detoxification makes LMs more brittle to distribution shift, they say . |
Learn to Refuse: Making Large Language Models More Controllable and Reliable through Knowledge Scope Limitation and Refusal Mechanism (2024.emnlp-main)
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| Challenge: | Recent advances in large language models have demonstrated impressive language understanding and generation capabilities, enabling them to answer a wide range of questions across various domains. |
| Approach: | They propose a refusal mechanism that instructs LLMs to refuse to answer challenging questions in order to avoid errors. |
| Outcome: | The proposed approach improves the controllability and reliability of large language models and their ability to answer questions across domains. |
Controllable Text Generation with Focused Variation (2020.findings-emnlp)
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Lei Shu, Alexandros Papangelis, Yi-Chia Wang, Gokhan Tur, Hu Xu, Zhaleh Feizollahi, Bing Liu, Piero Molino
| Challenge: | Focused-Variation Network (FVN) is a new model to control language generation. |
| Approach: | They propose a model that learns discrete latent spaces for each attribute inside codebooks and uses them to generate fluent text. |
| Outcome: | The proposed model can generate fluent and mostly coherent text on two text generation datasets with annotated content and style, and show state-of-the-art performance as assessed by automatic and human evaluations. |
ControlSpeech: Towards Simultaneous and Independent Zero-shot Speaker Cloning and Zero-shot Language Style Control (2025.acl-long)
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Shengpeng Ji, Qian Chen, Wen Wang, Jialong Zuo, Minghui Fang, Ziyue Jiang, Hai Huang, Zehan Wang, Xize Cheng, Siqi Zheng, Zhou Zhao
| Challenge: | Prior zero-shot TTS models only mimic the speaker’s voice without further control and adjustment capabilities while prior controllable TTS systems cannot perform speaker-specific voice generation. |
| Approach: | They propose a style control module that captures codec representations corresponding to timbre, content, and style in a discrete decoupling codec space. |
| Outcome: | The proposed system can fully clone the speaker's voice and perform speech-specific adjustment and control functions. |
Controllable Conversation Generation with Conversation Structures via Diffusion Models (2023.findings-acl)
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| Challenge: | Current generation models fail to effectively utilize rich linguistic and world knowledge to generate coherent long text. |
| Approach: | They propose a conversation generation framework that incorporates human knowledge and conversation structures with both controllability and interpretability for better conversation generation. |
| Outcome: | The proposed framework incorporates human knowledge and conversation structures with both controllability and interpretability for better conversation generation. |
Guiding the Growth: Difficulty-Controllable Question Generation through Step-by-Step Rewriting (2021.acl-long)
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| Challenge: | Existing QG systems perform substantially worse in answering multi-hop questions than single-hop ones. |
| Approach: | They propose a framework that progressively increases question difficulty through step-by-step rewriting under the guidance of an extracted reasoning chain. |
| Outcome: | The proposed framework increases question difficulty through step-by-step rewriting under the guidance of an extracted reasoning chain. |
Controllable Open-ended Question Generation with A New Question Type Ontology (2021.acl-long)
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| Challenge: | Existing question types are limited to generating multiple-sense questions . we present a question type-aware question generation framework to generate open-ended questions based on multiple-phrase questions - a task that is less explored . |
| Approach: | They propose a question type-aware question generation framework which predicts question focuses and produces the question. |
| Outcome: | The proposed model improves question quality over competitive comparisons on large-scale datasets. |
Author-in-the-Loop Response Generation and Evaluation: Integrating Author Expertise and Intent in Responses to Peer Review (2026.acl-long)
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| Challenge: | Existing ARG work lacks author inputs and controls and no evaluation measures response reflection of author signals and effectiveness in addressing reviewer concerns. |
| Approach: | They propose a novel author-in-the-loop framework that integrates domain expertise and author-only information into author response generation (ARG) they also propose re3Align, a large-scale dataset of aligned review–response–revision triplets, where revisions proxy author signals and REspGen, an author- in-the loop ARG framework supporting flexible author input, multi-attribute control, and evaluation-guided refinement. |
| Outcome: | Experiments with SOTA LLMs show that author input and evaluation-guided refinement improves author response quality and controllability–quality trade-offs. |
Tag-Instruct: Controlled Instruction Complexity Enhancement through Structure-based Augmentation (2025.findings-acl)
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| Challenge: | High-quality instruction data is crucial for developing large language models (LLMs), yet existing approaches struggle to effectively control instruction complexity. |
| Approach: | They propose a framework that compresses instructions into a compact tag space and enhances complexity through RL-guided tag expansion. |
| Outcome: | The proposed framework outperforms existing methods in the evaluation of instruction complexity augmentation and semantic compression of text into a compact tag space. |
ECO Decoding: Entropy-Based Control for Controllability and Fluency in Controllable Dialogue Generation (2025.emnlp-main)
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| Challenge: | Controllable Dialogue Generation (CDG) enables chatbots to generate responses tailored to desired attributes like emotion and dialog-act. |
| Approach: | They propose a method which dynamically adjusts the control strength at each generation step according to the model’s entropy in both the language model and attribute classifier probability distributions. |
| Outcome: | The proposed method outperforms existing methods on DailyDialog and MultiWOZ datasets while maintaining fluency and grammar. |
MessToClean: Evidence-Grounded Structure-Preserving Reconstruction for Real-World Degraded Exam Paper Images (2026.acl-long)
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Jiayi Tuo, Cheng Tang, Zihan Wang, Chenyue Zhou, Yao Li, Yanbiao Ma, Chao Wang, Wei Dai, Mingxuan Wang, Shitong Qin, Ziwei Zhao
| Challenge: | Existing Multimodal Large Language Models (MLLMs) fail under RDEI, leading to disrupted structure and evidence-unsupported hallucinations. |
| Approach: | They propose a backbone-agnostic, evidence-driven pipeline that treats off-the-shelf MLLMs as interchangeable components to improve stem consistency and figure consistency. |
| Outcome: | The proposed pipeline improves stem consistency by 1.01-3.18%, figure consistency by 0.50-49.16%, and refusal F1 by 1.06-10.88% across question types. |