Reward-Augmented Decoding: Efficient Controlled Text Generation With a Unidirectional Reward Model (2023.emnlp-main)
Copied to clipboard
| Challenge: | Large language models generate text that is problematic or lacks a desired attribute. |
| Approach: | They propose a text generation procedure that uses a small unidirectional reward model to encourage a language model to generate text with certain properties. |
| Outcome: | The proposed procedure outperforms prior weighted decoding methods and matches state-of-the-art techniques that require additional training. |
Similar Papers
Reward Gaming in Conditional Text Generation (2023.acl-long)
Copied to clipboard
| Challenge: | Recent work has used reward functions learned from human annotations to align conditional text generation models with desired behaviors. |
| Approach: | They propose to use reinforcement learning to train conditional text generation models with reward functions learned from human annotations to align outputs with desired behaviors. |
| Outcome: | The proposed framework improves the quality of generated summaries by using saliency and faithfulness metrics. |
Unlocking Anticipatory Text Generation: A Constrained Approach for Large Language Models Decoding (2024.emnlp-main)
Copied to clipboard
| Challenge: | Large language models have shown a powerful ability for text generation, but undesired behaviors such as toxicity and hallucinations can manifest. |
| Approach: | They propose to formalize text generation as a future-constrained generation problem to minimize undesirable behaviors and enforce faithfulness to instructions. |
| Outcome: | The proposed approach is effective across three tasks, including keyword-constrained generation, toxicity reduction, and factual correctness in question-answering. |
RARR: Researching and Revising What Language Models Say, Using Language Models (2023.acl-long)
Copied to clipboard
Luyu Gao, Zhuyun Dai, Panupong Pasupat, Anthony Chen, Arun Tejasvi Chaganty, Yicheng Fan, Vincent Zhao, Ni Lao, Hongrae Lee, Da-Cheng Juan, Kelvin Guu
| Challenge: | Language models (LMs) excel at many tasks but often produce unsupported or misleading content. |
| Approach: | They propose a system that finds attribution for any text generation model and post-edits it to fix unsupported content. |
| Outcome: | The proposed system improves attribution while preserving the original output. |
A Frustratingly Simple Decoding Method for Neural Text Generation (2024.lrec-main)
Copied to clipboard
| Challenge: | Neural text generation is notorious for repetitive loops and tedious outputs. |
| Approach: | They propose a method that penalizes future generation of repetitive content . they construct an anti-LM based on previously generated text . |
| Outcome: | The proposed method outperforms established baselines in terms of generation quality, decoding speed, and universality. |
Plug-in Language Model: Controlling Text Generation with a Simple Regression Model (2024.findings-naacl)
Copied to clipboard
| Challenge: | Large-scale pre-trained language models have demonstrated unrivaled capacity in generating text that closely resembles human-written content. |
| Approach: | They propose a plug-in language model that leverages reinforcement learning to adjust latent states to control text generation. |
| Outcome: | The proposed model outperforms existing methods that rely on gradient-based, weighted decoding, or prompt-based methods. |
How Helpful is Inverse Reinforcement Learning for Table-to-Text Generation? (2021.acl-short)
Copied to clipboard
| Challenge: | Existing approaches to Table-to-Text generation suffer from issues such as missing information, repetition and repetition. |
| Approach: | They propose to use Inverse Reinforcement Learning (IRL) to solve the Table-to-Text task . they use multiple interpretable unsupervised reward components that are combined linearly to form a composite reward function. |
| Outcome: | The proposed task outperforms strong RL baselines marginally in the Table-to-Text task. |
Mapping Language to Programs using Multiple Reward Components with Inverse Reinforcement Learning (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches focus on likelihood-based training or using reinforcement learning to fine-tune models based on a single reward. |
| Approach: | They propose an approach to fine-tune programs from natural language instruction . they propose a reward function that linearly combines them and a policy for program generation . |
| Outcome: | The proposed approach achieves better performance than competing methods using Reinforcement Learning. |
RED: Unleashing Token-Level Rewards from Holistic Feedback via Reward Redistribution (2025.emnlp-main)
Copied to clipboard
| Challenge: | Experimental results demonstrate the superiority of our approach to aligning large language models with human preferences. |
| Approach: | They propose a method that evaluates and assigns specific credit to each token using an off-the-shelf reward model. |
| Outcome: | The proposed method evaluates and assigns specific credit to each token using an off-the-shelf reward model. |
Reinforcement Learning with Token-level Feedback for Controllable Text Generation (2024.findings-naacl)
Copied to clipboard
| Challenge: | Existing methods for controllable text generation are guided by coarse-grained feedback, which may lead to suboptimal performance owing to semantic twists or progressions within sentences. |
| Approach: | They propose a reinforcement learning algorithm which formulates TOken-LEvel rewards for controllable text generation and employs a "first-quantize-then-noise" paradigm to enhance the robustness of the RL algorithm. |
| Outcome: | The proposed algorithm can achieve superior performance on single-attribute and multi-attract control tasks. |
Navigating Noisy Feedback: Enhancing Reinforcement Learning with Error-Prone Language Models (2024.findings-emnlp)
Copied to clipboard
Muhan Lin, Shuyang Shi, Yue Guo, Behdad Chalaki, Vaishnav Tadiparthi, Ehsan Moradi Pari, Simon Stepputtis, Joseph Campbell, Katia Sycara
| Challenge: | Reward hacking is a problem in reinforcement learning where the ability to specify the desired behavior of a reward function is difficult. |
| Approach: | They propose to use feedback as a potential-based shaping function to solicit and apply feedback from large language models to improve convergence speed and policy returns. |
| Outcome: | The proposed method improves convergence speed and policy returns over baselines even with significant ranking errors and eliminates the need for complex post-processing of reward functions. |