distributivity does not. Answer the question using the fewest possible tools. Only include the ...
Few-shot learning:This term focuses on the idea that the learning algorithm is provided with only...
On the other hand, if we use dynamic few-shot prompt, we just use the most relevant examples (for the given user input) in the prompt. For an example we can decide that we only want to use the top 3 most relevant examples in the prompt. To implement this strategy we just nee...
we recommend carefully looking at a few sample outputs using--limit 10first to confirm answer extraction and scoring on generative tasks is performing as expected. providingsystem="<some system prompt here>"within--model_argsfor anthropic-chat-completions, to instruct the model what format to ...
Most untrained medical professionals will not know the answer- (a) is cancerous, while (b) is benign. So, data labeling is more difficult in such scenarios. At best, we would have only a handful of annotated samples, which is not nearly enough to train supervised learning models. ...
Now, we aim to answer "how to cost-effectively utilize limited data for defect detection?" . Inspired by positive and unlabeled learning and few-shot learning, we propose a Positive Unlabeled learning based Few-shot Anomaly Detection model (PUFAD) that builds a representative memory bank of ...
the evaluation of the Med-Flamingo model, we were concerned that there may be leakage between the pre-training datasets (PMC-OA and MTB) and the down-stream VQA datasets used for evaluation; this could inflate judgements of model quality, as the model could memorize image-question-answer ...
You are given a math word problem and you are supposed to only use subtraction on the numbers embedded in the text to answer the following question and then only report the final numerical answer. Context: Jake has 8 fewer peaches and 10 more apples than Steven. Steven has 11 apples and ...
Upon manual inspection, however, we found that for every overlap we inspected, in all 3 datasets, the source text was present in our training data but the question/answer pairs were not, meaning the model gains only background information and cannot memorize the answer to a specific question....
("verbalizer_nlls" to get the NLL of the true answer after eliminating tokens that aren't "verbalizer" tokens or class names; "verbalizer_accs" to get the accuracy when only consider the probabilities of classes with class names instead of all possible tokens)python extract_fields.py -...