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Found 4 papers, 1 papers with code
Task Facet Learning: A Structured Approach to Prompt Optimization
Given a task in the form of a basic description and its training examples, prompt optimization is the problem of synthesizing the given information into a text prompt for a large language model (LLM).
$\texttt{LM}^\texttt{2}$: A Simple Society of Language Models Solves Complex Reasoning
The solver model generates the solution to the subproblems that are then checked by the verifier module; depending upon the feedback from the verifier, the reasoning context is constructed using the subproblems and the solutions.
Prompt-Propose-Verify: A Reliable Hand-Object-Interaction Data Generation Framework using Foundational Models
Diffusion models when conditioned on text prompts, generate realistic-looking images with intricate details.
Small Language Models Fine-tuned to Coordinate Larger Language Models improve Complex Reasoning
Additionally, we show that DaSLaM is not limited by the solver's capabilities as a function of scale; e. g., solver LMs with diverse sizes give significant performance improvement with our solver-agnostic decomposition technique.
Ranked #6 on
Overall - Test
on JEEBench
(using extra training data)
