Papers by Nicola Dainese
In-Context Symbolic Regression: Leveraging Large Language Models for Function Discovery (2024.acl-srw)
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| Challenge: | State of the art Symbolic Regression (SR) methods build specialized models, while the application of Large Language Models (LLMs) remains largely unexplored. |
| Approach: | They propose a framework which iteratively refines a functional form with an LLM and determines its coefficients with an external optimizer. |
| Outcome: | The proposed method outperforms the best SR methods on four popular benchmarks while yielding simpler equations with better out of distribution generalization. |
Can docstring reformulation with an LLM improve code generation? (2024.eacl-srw)
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| Challenge: | Existing approaches focus on training, fine-tuning or prompting LLMs to generate better outputs given the same input. |
| Approach: | They propose to optimize part of the input, the docstring, via reformulation with an LLM to improve code generation. |
| Outcome: | The proposed methods improve code generation on the original HumanEval benchmark and multiple curated variants on the same input. |
Reader: Model-based language-instructed reinforcement learning (2023.emnlp-main)
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| Challenge: | Existing models of RL are limited and need to be re-trained for every new problem. |
| Approach: | They propose a model-based reinforcement learning approach to tackle the environment Read To Fight Monsters, a grounded policy learning problem. |
| Outcome: | The proposed approach performs better than existing model-free SOTA agents in the read to fight monsters environment and is more sample efficient than existing models. |