Papers with open-sourced language models
Unveiling Imitation Learning: Exploring the impact of Data Falsity to Large Language Model (2024.findings-acl)
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| Challenge: | Recent studies have focused on improving open-source language models through imitation learning. |
| Approach: | They propose to use false pairs to control the factuality ratio of a dataset to study noise. |
| Outcome: | The proposed model is based on a Falsity-Controllable dataset with false pairs and false pairs to control the factuality ratio. |
Eliciting Instruction-tuned Code Language Models’ Capabilities to Utilize Auxiliary Function for Code Generation (2024.findings-emnlp)
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| Challenge: | Using auxiliary functions to implement functions is important for instruction-tuned models because it reduces the implementation difficulty of a target function compared to implementing them from scratch. |
| Approach: | They propose several ways to provide auxiliary functions to the models by adding them to the query or providing a response prefix to incorporate the ability to utilize auxiliary function with the instruction following capability. |
| Outcome: | The proposed models outperform the recent powerful language models, gpt-4o, in the code generation task. |