This repo aims to record advanced papers of controllable and decoding generation in LLMs.
We strongly encourage the researchers who want to promote their fantastic work in this area to make pull requests to update their paper's information!
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Controllable Neural Text Generation
Lilian Weng
Lil'Log, 2021. [Link] -
A Survey of Controllable Text Generation using Transformer-based Pre-trained Language Models
Hanqing Zhang, Haolin Song, Shaoyu Li, Ming Zhou, Dawei Song
ACM Computing Surveys, 2023. [Paper] -
From Decoding to Meta-Generation: Inference-time Algorithms for Large Language Models
Sean Welleck, Amanda Bertsch, Matthew Finlayson, Hailey Schoelkopf, Alex Xie, Graham Neubig, Ilia Kulikov, Zaid Harchaoui
arXiv 2024. [Paper]
- Controllable Text Generation with Language Constraints
Howard Chen, Huihan Li, Danqi Chen, Karthik Narasimhan
arXiv 2022. [Paper] [Github]
- LLM Critics Help Catch LLM Bugs
Nat McAleese, Rai Michael Pokorny, Juan Felipe Ceron Uribe, Evgenia Nitishinskaya, Maja Trebacz, Jan Leike
Jun 28, 2024. OpenAI. [Paper] [Link]
- Decoding-time Realignment of Language Models
Tianlin Liu, Shangmin Guo, Leonardo Bianco, Daniele Calandriello, Quentin Berthet, Felipe Llinares, Jessica Hoffmann, Lucas Dixon, Michal Valko, Mathieu Blondel
ICML 2024. [Paper]
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DeAL: Decoding-time Alignment for Large Language Models
James Y. Huang, Sailik Sengupta, Daniele Bonadiman, Yi-an Lai, Arshit Gupta, Nikolaos Pappas, Saab Mansour, Katrin Kirchhoff, Dan Roth
arXiv 2024. [Paper] -
Decoding-Time Language Model Alignment with Multiple Objectives
Ruizhe Shi, Yifang Chen, Yushi Hu, Alisa Liu, Hannaneh Hajishirzi, Noah A. Smith, Simon Du
arXiv 2024. [Paper] [Github] -
Cascade Reward Sampling for Efficient Decoding-Time Alignment
Bolian Li, Yifan Wang, Ananth Grama, Ruqi Zhang
arXiv 2024. [Paper] [Github] -
Reward Steering with Evolutionary Heuristics for Decoding-time Alignment
Anonymous ACL submission
arXiv 2024. [Paper]
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DOLA: DECODING BY CONTRASTING LAYERS IMPROVES FACTUALITY IN LARGE LANGUAGE MODELS
Yung-Sung Chuang, Yujia Xie, Hongyin Luo, Yoon Kim, James Glass, Pengcheng He
ICLR 2024. [Paper] [Github] -
Inference-Time Intervention: Eliciting Truthful Answers from a Language Model
Kenneth Li, Oam Patel, Fernanda Viégas, Hanspeter Pfister, Martin Wattenberg
NeurIPS 2023. [Paper] [Github]
- TruthX: Alleviating Hallucinations by Editing Large Language Models in Truthful Space
Shaolei Zhang, Tian Yu, Yang Feng
ACL 2024. [Paper] [Github]
- Truth Forest: Toward Multi-Scale Truthfulness in Large Language Models through Intervention without Tuning
Zhongzhi Chen, Xingwu Sun, Xianfeng Jiao, Fengzong Lian, Zhanhui Kang, Di Wang, Cheng-Zhong Xu
AAAI 2024. [Paper] [Github]
- Lookback Lens: Detecting and Mitigating Contextual Hallucinations in Large Language Models Using Only Attention Maps
Yung-Sung Chuang, Linlu Qiu, Cheng-Yu Hsieh, Ranjay Krishna, Yoon Kim, James Glass
Arxiv 2024. [Paper] [Github]
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Trusting Your Evidence: Hallucinate Less with Context-aware Decoding
Weijia Shi, Xiaochuang Han, Mike Lewis, Yulia Tsvetkov, Luke Zettlemoyer, Scott Wen-tau Yih
NAACL 2024. [Paper] [Github] -
Mutual Information Alleviates Hallucinations in Abstractive Summarization
Liam van der Poel, Ryan Cotterell, Clara Meister
EMNLP 2022. [Paper] [Github] -
Locating and Editing Factual Associations in GPT
Kevin Meng, David Bau, Alex Andonian, Yonatan Belinkov
NeurIPS 2022. [Paper] [Website]
- Aligning Large Language Models with Representation Editing: A Control Perspective
Lingkai Kong, Haorui Wang, Wenhao Mu, Yuanqi Du, Yuchen Zhuang, Yifei Zhou, Yue Song, Rongzhi Zhang, Kai Wang, Chao Zhang
Arxiv 2024. [Paper]
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PaCE: Parsimonious Concept Engineering for Large Language Models
Jinqi Luo, Tianjiao Ding, Kwan Ho Ryan Chan, Darshan Thaker, Aditya Chattopadhyay, Chris Callison-Burch, René Vidal
arXiv 2024. [Paper] [Github] -
Controlled Decoding from Language Models
Sidharth Mudgal, Jong Lee, Harish Ganapathy, YaGuang Li, Tao Wang, Yanping Huang, Zhifeng Chen, Heng-Tze Cheng, Michael Collins, Trevor Strohman, Jilin Chen, Alex Beutel, Ahmad Beirami
ICML 2024. [Paper]
- Weak-to-Strong Search: Align Large Language Models via Searching over Small Language Models
Zhanhui Zhou, Zhixuan Liu, Jie Liu, Zhichen Dong, Chao Yang, Yu Qiao
arXiv 2024 [Paper] [Github]
- BWArea Model: Learning World Model, Inverse Dynamics, and Policy for Controllable Language Generation
Chengxing Jia, Pengyuan Wang, Ziniu Li, Yi-Chen Li, Zhilong Zhang, Nan Tang, Yang Yu
Arxiv 2024. [Paper]
- COLD-Attack: Jailbreaking LLMs with Stealthiness and Controllability
Xingang Guo, Fangxu Yu, Huan Zhang, Lianhui Qin, Bin Hu
ICML 2024. [Paper] [Github]
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Controlled Text Generation for Large Language Model with Dynamic Attribute Graphs
Xun Liang, Hanyu Wang, Shichao Song, Mengting Hu, Xunzhi Wang, Zhiyu Li, Feiyu Xiong, Bo Tang
ACL 2024. [Ppaer] [Github] -
Controlled Text Generation via Language Model Arithmetic
Jasper Dekoninck, Marc Fischer, Luca Beurer-Kellner, Martin Vechev
ICLR 2024. [Paper] [Github] -
Guiding LLMs The Right Way: Fast, Non-Invasive Constrained Generation
Luca Beurer-Kellner, Marc Fischer, Martin Vechev
arXiv 2024. [Paper]
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Controllable Text Generation with Neurally-Decomposed Oracle
Tao Meng, Sidi Lu, Nanyun Peng, Kai-Wei Chang
NeurIPS 2022. [Paper] [Github] -
BOLT: Fast Energy-based Controlled Text Generation with Tunable Biases
Xin Liu, Muhammad Khalifa, Lu Wang
ACL 2023. [Paper] [Github] -
Controllable Text Generation with Neurally-Decomposed Oracle
Xin Liu, Muhammad Khalifa, Lu Wang
NeurIPS 2022. [Paper] [Github] -
COLD Decoding: Energy-based Constrained Text Generation with Langevin Dynamics
Lianhui Qin, Sean Welleck, Daniel Khashabi, Yejin Choi
NeurIPS 2022 [Paper] [Github]
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Gradient-Based Constrained Sampling from Language Models
Sachin Kumar, Biswajit Paria, Yulia Tsvetkov
EMNLP 2022. [Paper] [Github] -
FUDGE: Controlled Text Generation With Future Discriminators
Kevin Yang, Dan Klein
NAACL 2021 [Paper] [Github]
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A Distributional Approach to Controlled Text Generation
Muhammad Khalifa, Hady Elsahar, Marc Dymetman
ICLR 2021. [Paper] [Github] -
Plug and Play Language Models: A Simple Approach to Controlled Text Generation
Sumanth Dathathri, Andrea Madotto, Janice Lan, Jane Hung, Eric Frank, Piero Molino, Jason Yosinski, Rosanne Liu
ICLR 2020 [Paper] [Github]
- Controlled Text Generation as Continuous Optimization with Multiple Constraints
Sachin Kumar, Eric Malmi, Aliaksei Severyn, Yulia Tsvetkov
NeurIPS 2020. [Paper] [Github]
- Tractable Control for Autoregressive Language Generation
Honghua Zhang, Meihua Dang, Nanyun Peng, Guy Van den Broeck
ICML 2023 [Paper] [Github]
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An Extensible Plug-and-Play Method for Multi-Aspect Controllable Text Generation
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Xuancheng Huang, Zijun Liu, Peng Li, Tao Li, Maosong Sun, Yang Liu
ACL 2023. [Paper] [Github] -
A Distributional Lens for Multi-Aspect Controllable Text Generation
Yuxuan Gu, Xiaocheng Feng, Sicheng Ma, Lingyuan Zhang, Heng Gong, Bing Qin
EMNLP 2022. [Paper] [Github]
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Teaching Language Models to Hallucinate Less with Synthetic Tasks
Erik Jones, Hamid Palangi, Clarisse Simões, Varun Chandrasekaran, Subhabrata Mukherjee, Arindam Mitra, Ahmed Awadallah, Ece Kamar
ICLR 2024. [Paper] -
Knowledge Overshadowing Causes Amalgamated Hallucination in Large Language Models
Yuji Zhang, Sha Li, Jiateng Liu, Pengfei Yu, Yi R. Fung, Jing Li, Manling Li, Heng Ji
arXiv 2024. [Paper]
- Critic-Guided Decoding for Controlled Text Generation
Minbeom Kim, Hwanhee Lee, Kang Min Yoo, Joonsuk Park, Hwaran Lee, Kyomin Jung
ACL 2023. [Paper] [Github]
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COLLIE: Systematic Construction of Constrained Text Generation Tasks
Shunyu Yao, Howard Chen, Austin W. Hanjie, Runzhe Yang, Karthik Narasimhan
ICLR 2024. [Paper] [Github] -
Controlled Text Generation with Natural Language Instructions
Wangchunshu Zhou, Yuchen Eleanor Jiang, Ethan Wilcox, Ryan Cotterell, Mrinmaya Sachan
ICML 2023. [Paper] [Github] -
Toward Unified Controllable Text Generation via Regular Expression Instruction
Xin Zheng, Hongyu Lin, Xianpei Han, Le Sun
IJCNLP-AACL 2023. [Paper] [Github]
- Xiang Lisa Li, John Thickstun, Ishaan Gulrajani, Percy Liang, Tatsunori B. Hashimoto
Diffusion-LM Improves Controllable Text Generation
NeurIPS 2022. [Paper] [Github]
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NeuroComparatives: Neuro-Symbolic Distillation of Comparative Knowledge
Phillip Howard, Junlin Wang, Vasudev Lal, Gadi Singer, Yejin Choi, Swabha Swayamdipta
NACCL 2024. [Paper] [Github] -
NeuroLogic Decoding: (Un)supervised Neural Text Generation with Predicate Logic Constraints
Ximing Lu, Peter West, Rowan Zellers, Ronan Le Bras, Chandra Bhagavatula, Yejin Choi
NAACL 2021. [Paper] [Github]
- "We Need Structured Output": Towards User-centered Constraints on Large Language Model Output
Michael Xieyang Liu, Frederick Liu, Alexander J. Fiannaca, Terry Koo, Lucas Dixon, Michael Terry, Carrie J. Cai
CHI EA 2024. [Paper]
- Evaluating, Understanding, and Improving Constrained Text Generation for Large Language Models
Xiang Chen, Xiaojun Wan
Arxiv 2023. [Paper]
- Evaluating Large Language Models on Controlled Generation Tasks
Jiao Sun, Yufei Tian, Wangchunshu Zhou, Nan Xu, Qian Hu, Rahul Gupta, John Frederick Wieting, Nanyun Peng, Xuezhe Ma
EMNLP 2023. [Paper] [Github]
- JAMDEC: Unsupervised Authorship Obfuscation using Constrained Decoding over Small Language Models
Jillian Fisher, Ximing Lu, Jaehun Jung, Liwei Jiang, Zaid Harchaoui, Yejin Choi
NAACL 2024. [Paper] [Github]
- Semantically-Aware Constrained Decoding for Code Generation
Kristian Muñiz
March 2024. [Link]
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Discrete Prompt Optimization via Constrained Generation for Zero-shot Re-ranker
Sukmin Cho, Soyeong Jeong, Jeongyeon Seo, Jong C. Park
ACL 2023. [Paper] [Github] -
Synchromesh: Reliable code generation from pre-trained language models
Gabriel Poesia, Oleksandr Polozov, Vu Le, Ashish Tiwari, Gustavo Soares, Christopher Meek, Sumit Gulwani
ICLR 2022. [Paper] [Github]