Papers with online applications
Continual Reinforcement Learning for Controlled Text Generation (2024.lrec-main)
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| Challenge: | Controlled Text Generation (CTG) aims to steer text generation towards texts possessing a desired attribute. |
| Approach: | They propose an algorithm that steers the generation of continuations of a given context . they propose a Continual Learning problem to learn at every step to steer next-word generation . |
| Outcome: | The proposed algorithm is based on a plug-and-play language model and exhibits promising results. |
Tree-of-Code: A Self-Growing Tree Framework for End-to-End Code Generation and Execution in Complex Tasks (2025.findings-acl)
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| Challenge: | Effectively and efficiently handling complex realworld problems has become a key focus across industry and academia. |
| Approach: | They propose a tree-of-code framework that generates nodes through self-supervision and combines prompt and model exploration in a GT-free setting. |
| Outcome: | Experiments on two datasets with ten popular zero-shot LLMs show that Tree-of-Code boosts accuracy by nearly 20% over CodeAct with fewer than 1/4 turns. |
On the Limitations of Reference-Free Evaluations of Generated Text (2022.emnlp-main)
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| Challenge: | a recent study has shown that evaluation metrics which accurately estimate the quality of generated text are limited in their ability to evaluate generated text. |
| Approach: | They argue that reference-free metrics are limited in their ability to evaluate generated text . they recommend that they be used as diagnostic tools for analyzing and understanding model behavior . |
| Outcome: | The proposed evaluation metrics are limited in their ability to evaluate generated text . they can be optimized at test time, can be biased against models with similar outputs . |