Summary of Improving Linguistic Diversity Of Large Language Models with Possibility Exploration Fine-tuning, by Long Mai and Julie Carson-berndsen
Improving Linguistic Diversity of Large Language Models with Possibility Exploration Fine-Tuning
by Long Mai, Julie Carson-Berndsen
First submitted to arxiv on: 4 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a task-agnostic framework called Possibility Exploration Fine-Tuning (PEFT) to enhance the linguistic diversity of Large Language Models (LLMs). The current homogenization of viewpoints and underrepresentation of demographic groups in LLM outputs are concerns, as they replicate human-like abilities. To address this issue, previous techniques were fine-tuned for specific tasks or came with increased computational cost and latency. PEFT aims to generate multiple diverse responses while keeping latency and computation costs low. The framework fine-tunes models given the same prompt, controlling possibility numbers. Experiments on dialogue and story generation demonstrate PEFT’s success in enhancing diversity, reducing similarity between candidate responses, and reducing demographic bias. This approach is crucial for applications requiring very low latency, such as chatbots and virtual assistants. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make computers better at talking like humans. Right now, these computer models can sound very similar and don’t always include diverse perspectives or voices. To fix this, researchers have tried different ways to fine-tune the models, but they often require a lot of computation power and time. The new method, called PEFT, is designed to be more flexible and efficient. It helps computers generate many different responses to the same prompt, which can include more diverse viewpoints and voices. This is important for applications like chatbots that need to respond quickly and accurately. |
Keywords
» Artificial intelligence » Fine tuning » Prompt
