levnikolaevich
commented
5 months ago 1. Define your task.
llm ["microsoft/Phi-3-mini-4k-instruct","google/gemma-1.1-2b-it", "google/gemma-1.1-7b-it"]
We aim to use a language model for correcting and explaining errors in sentences written in English. The desired response format is JSON.
Input examples:
She don't know what to do next.
The team is needing a new coach for the next season.
Response examples:
{
"original_sentence": "She don't know what to do next.",
"llm_corrected_sentence": "She doesn't know what to do next.",
"llm_error_explanation": "The original sentence used 'don't' with a singular subject, which is incorrect. The correct verb form for the third person singular is 'doesn't'."
} {
"original_sentence": "The team is needing a new coach for the next season.",
"llm_corrected_sentence": "The team needs a new coach for the next season.",
"llm_error_explanation": "The original sentence used 'is needing', which is not standard because 'need' typically does not use the continuous form. 'Needs' is the correct form here."
}One of the requirements for the task is to use language models with open weights and a small number of parameters (up to 8 million) in order to run on less demanding hardware. Since error analysis can be organized in the background, the response time for each error can be limited to 1-5 minutes.
2. Define your pipeline. At the first stage, it is planned to use a simple program called dspy with the module e dspy.ChainofThought.
================= we are here ================= -->Then write your (initial) DSPy program. Again: start simple, and let the next few steps guide any complexity you will add.
- Explore a few examples.
- Define your data.
- Define your metric.
- Collect preliminary "zero-shot" evaluations.
- Compile with a DSPy optimizer.
- Iterate.
Datos de entrada /Oración/
Respuesta: The correct sentence would be as follows: /Oración/
Errors: 1. 2. 3.
Como opción, devolver la respuesta en json (como lo hace LangTools).
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