COMPUTING N-DIMENSIONAL SENTIMENT USING A LARGE LANGUAGE MODEL
Approaches for generating predictions related to a set of input text are provided. Text can be received. A machine learning model can be utilized to determine a set of classification probabilities of the text relative to a set of anchor points. A sentiment score indicative of an emotional content of the text can be determined based, at least in part, upon a convex combination of the set of probabilities for the text. One or more predictions related to the text can be generated.
1 . A computer-implemented method, comprising:
receiving text;
using a machine learning model to determine a set of classification probabilities of the text relative to a set of anchor points; and
determining a sentiment score indicative of an emotional content of the text based, at least in part, upon a convex combination of the set of anchor points, using the set of probabilities for the text.
2 . The computer-implemented method of claim 1 , wherein the classification probabilities are computed using the output of a large language model.
3 . The computer-implemented method of claim 2 , wherein the large language model is fine-tuned on a multi-genre natural language inference (MNLI) dataset.
4 . The computer-implemented method of claim 1 , further comprising:
generating one or more predictions related to the text.
5 . The computer-implemented method of claim 4 , wherein the one or more predictions is associated with a response to an administered therapy for treatment-resistant depression.
6 . The computer-implemented method of claim 1 , wherein the sentiment scores include arousal scores, valence scores, and confidence scores for individual pieces of the text.
7 . A computing system, comprising:
a computing device processor; and
a memory device including instructions that, when executed by the computing device processor, enable the computing system to:
receive text;
use a machine learning model to determine a set of classification probabilities of the text relative to a set of anchor points; and
determine a sentiment score indicative of an emotional content of the text based, at least in part, upon a convex combination of the set of set of anchor points, using the set of probabilities for the text.
8 . The computing system of claim 7 , wherein the classification probabilities are computed using the output of a large language model.
9 . The computing system of claim 8 , wherein the large language model is fine-tuned on a multi-genre natural language inference (MNLI) dataset.
10 . The computing system of claim 7 , wherein the instructions that, when executed by the computing device, enable the computing system to further:
generate one or more predictions related to the text.
11 . The computing system of claim 7 , wherein the sentiment scores include arousal scores, valence scores, and confidence scores for individual pieces of the text.
12 . The computing system of claim 10 , wherein the one or more predictions is associated with a response to an administered therapy for treatment-resistant depression.
13 . The computing system of claim 7 , wherein the instructions that, when executed by the computing device processor, enable the computing system to further:
pass the text through a classifier;
compute probability values indicative of a probability that a string of text, of the text, is within a class of one or more classes;
separate the one or more classes into a set of lists corresponding to the set of anchor points; and
generate weights for individual strings of text based, at least in part, upon the set of lists.
14 . A non-transitory computer-readable medium storing instructions that, when executed by at least one processor, cause the at least one processor to:
receive text;
use a machine learning model to determine a set of classification probabilities of the text relative to a set of anchor points; and
determine a sentiment score indicative of an emotional content of the text based, at least in part, upon a convex combination of the anchor points, using the set of probabilities for the text.
15 . The non-transitory computer-readable medium of claim 14 , wherein the sentiment score is determined using a classifier built from a large language model.
16 . The non-transitory computer-readable medium of claim 15 , wherein the large language model is fine-tuned on a multi-genre natural language inference (MNLI) dataset.
17 . The non-transitory computer-readable medium of claim 14 , wherein the instructions, when executed by the at least one processor, cause the at least one processor to further:
generate one or more predictions related to the text.
18 . The non-transitory computer-readable medium of claim 14 , wherein the sentiment scores include arousal scores, valence scores, and confidence scores for individual pieces of the text.
19 . The non-transitory computer-readable medium of claim 17 , wherein the one or more predictions is associated with a response to an administered therapy for treatment-resistant depression.
20 . The non-transitory computer-readable medium of claim 14 , wherein the instructions, when executed by the at least one processor, cause the at least one processor to further:
pass the text through a classifier;
compute probability values indicative of a probability that a string of text, of the set of text, is within a class of one or more classes;
separate the one or more classes into a set of lists corresponding to the set of anchor points; and
generate weights for individual strings of text based, at least in part, upon the set of lists.