This study addresses the critical issue of factual inaccuracies in machine-generated text summaries, an increasingly prevalent issue in information dissemination. Recognizing the potential of such errors to compromise information reliability, we investigate the nature of factual inconsistencies across machinesummarized content. We introduce a prompt-based classification system that categorizes errors into four distinct types: misrepresentation, inaccurate quantities or measurements, false attribution, and fabrication. The participants are tasked with evaluating a corpus of machine-generated summaries against their original articles. Our methodology employs qualitative judgements to identify the occurrence of factual distortions. The results show that our prompt-based approaches are able to detect the type of errors in the summaries to some extent, although there is scope for improvement in our classification systems.
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In an era where information dissemination is predominantly driven by digital platforms, the accuracy and integrity of content have become paramount. Machine-generated summaries, designed to distill complex articles into digestible formats, have gained traction because of their eficiency and scalability. However, the susceptibility of these systems to introduce factual errors poses a significant challenge. This research endeavors to meticulously analyze the prevalence of factual inaccuracies within machine-generated summaries by establishing a systematic methodology for identification and categorization.
We propose a novel prompt-based framework [
This research serves as a critical investigation into the fidelity [
In recent years, the field of natural language processing (NLP) has seen a significant shift towards the development and utilization of large language models (LLMs). These LLMs, particularly exemplified by OpenAI’s GPT series (Generative Pre-trained Transformer), have revolutionized various NLP tasks. The foundational concept behind these models involves pre-training on vast amounts of text data, enabling them to learn intricate language patterns and structures.
Zero-shot prompting with LLMs has been leveraged across various tasks. In text generation,
these LLMs exhibit the capacity to produce coherent and contextually relevant content even
when prompted by unseen topics or styles. Translation tasks [
GPT-3.5 Turbo represents a significant advancement in the landscape of LLMs. It builds on
the foundation laid by GPT-3 [
We have been provided with the original articles and incorrect summaries in the training set and the testing set of ILSUM task 2. There are 8497 articles in the train set and 200 articles in the test set.
The task focuses on identifying factual errors in machine-generated summaries. The objective is to categorize each datapoint into diferent categories based on factual incorrectness in the summaries.
Possible types of factual incorrectness: • Misrepresentation: This involves presenting information in a way that is misleading or gives a false impression. This could be done by exaggerating certain aspects, understating others, or twisting facts to fit a particular narrative. • Inaccurate Quantities or Measurements: Factual incorrectness can occur when precise quantities, measurements, or statistics are misrepresented, whether by error or intent. • False Attribution: Incorrectly attributing a statement, idea, or action to a person or group is another form of factual incorrectness. • Fabrication: Making up data, sources, or events is a severe form of factual incorrectness.
Prompting is a valuable approach to solving multilabel classification problems for several reasons: – Natural Language Bridge: Prompting allows the use of natural language to bridge the gap between machine learning models and complex tasks without the need for extensive reprogramming or model redesign. It essentially converts the classification task into a text generation problem, which large language models are inherently good at solving. – Transfer Learning: Through prompting, models that have been trained on vast datasets can apply their learned knowledge to classify data across multiple labels. This transfer learning is eficient because it leverages pre-existing knowledge without the need for extensive additional training on specialized datasets. – Flexibility: Prompt-based approaches are highly flexible and easily adapted to diferent tasks and domains. This is particularly useful in multilabel classification, where the relationships and distinctions between categories can be nuanced and context-dependent. – Eficiency: Prompting can reduce the need for large annotated datasets that are typically required to train multi-label classifiers. By using prompts, models can often make predictions without any examples(Zero-Shot classification).
The prompting approach involved employing the GPT-3.5 Turbo model in zero-shot mode for the multi-label classification task of detecting factual incorrectness in machine-generated summaries. The approach included instructing the GPT-3.5 Turbo model in zero-shot mode, providing a set of label descriptions, and outlining the task to be executed. The hyperparameters are as follows: temperature = {0.5, 0.6, 0.7, 0.8, 0.9}, max-tokens = 50, and stop = None. A diagrammatic representation of the model is shown in Figure 1. The prompt we use for the model is provided in Figure 2.
Here we discuss the various prompt based algorithmic approaches that we took to attempt the problem of multi-label error classification:- 1 – Algorithm 1 tries to prompt the LLM to understand whether the given incorrect summary belongs to the class misrepresentation. Then the labels fabrication, false_attribution, and incorrect_quantities are checked, respectively. If we get one predicted label for a given incorrect summary, we stop the algorithm. The simple heuristic behind first checking whether the (incorrect summary, original document) pair belongs to class misrepresentation is the fact that the misrepresentation class occurs in higher proportions in the training data. The pseudocode of the algorithm is given in Algorithm 1. – Algorithm 2 tries to prompt the LLM in order to understand whether the given incorrect summary belongs to the class false_attribution. Then the labels misrepresentation, fabrication, and incorrect_quantities are checked, respectively. If we get one predicted label for a given incorrect summary, we stop the algorithm. The simple heuristic behind first checking whether the (incorrect summary, original document) pair belongs to the false_attribution class is the fact that the false_attribution class occurs in higher proportions in the training data. The pseudocode of the algorithm is given in Algorithm 2. – Algorithm 3 tries to prompt the LLM in order to understand whether the given incorrect summary belongs to the class misrepresentation. Then, the labels false_attribution, fabrication, and incorrect_quantities are checked, respectively. If we get two predicted labels for a given incorrect summary, we stop the algorithm. The simple heuristic behind ifrst checking whether the (incorrect summary, original document) pair belongs to the misrepresentation, and false_attribution class is the fact that the misrepresentation, and false_attribution class occurs in higher proportions in the training data. The pseudocode of the algorithm is given in Algorithm 3. – Algorithm 4 tries to prompt the LLM in order to understand whether the given incorrect summary belongs to the class misrepresentation. Then the labels fabrication, false_attribution, and incorrect_quantities are checked, respectively. If we get two predicted labels for a given incorrect summary we stop the algorithm.The simple heuristic behind first checking whether the (incorrect summary, original document) pair belongs to the misrepresentation, and fabrication class is the fact that the misrepresentation, and fabrication class occurs in higher proportions in the training data. The pseudocode of the algorithm is given in Algorithm 4. – Algorithm 5 tries to prompt the LLM in order to understand whether the given incorrect summary belongs to the class false_attribution. Then the labels misrepresentation, fabrication, and incorrect_quantities are checked, respectively. If we get four predicted labels for a given incorrect summary, we stop the algorithm. There can be data points for which we get less than four correct data points. We run GPT-3.5 Turbo at diferent temperatures 0.5, 0.6, 0.7, 0.8, and 0.9 respectively. Then we take an ensemble of the five output test runs being run at diferent temperatures by considering all the labels that 1We want to specify that all our results are non-deterministic in nature i.e. the same hyperparameter settings can lead to diferent results on diferent test runs.
occurred at least twice for a particular datapoint. The ensembling method that we tried helped in providing improved accuracy and generalization bringing more stability into the nature of outputs. The pseudocode of the algorithm is given in Algorithm 5.
else
end else
end
if prompted LLM to check whether the given article and the corresponding summary belong to class false_attribution then
Then output the class false_attribution counter(variable) = counter(variable)+1 if counter(variable)==1 for a datapoint then stop checking for the next label for that datapoint
Do not perform any action else end else else end else else end else end
Algorithm 1: Pseudocode
end
if prompted LLM to check whether the given article and the corresponding summary belong to class incorrect_quantities then
Then output the class incorrect_quantities counter(variable) = counter(variable)+1 if counter(variable)==1 for a datapoint then stop checking for the next label for that datapoint
( ) ← 0 ;
if prompted LLM to check whether the given article and the corresponding summary belong to class false_attribution then
Then output the class false_attribution counter(variable) = counter(variable)+1 if counter(variable)==1 for a datapoint then
stop checking for the next label for that datapoint else
end else
end
if prompted LLM to check whether the given article and the corresponding summary belong to class misrepresentation then
Then output the class misrepresentation counter(variable) = counter(variable)+1 if counter(variable)==1 for a datapoint then stop checking for the next label for that datapoint
end
if prompted LLM to check whether the given article and the corresponding summary belong to class fabrication then
Then output the class fabrication counter(variable) = counter(variable)+1 if counter(variable)==1 for a datapoint then stop checking for the next label for that datapoint
Do not perform any action else end else else end else else end else end
Algorithm 2: Pseudocode
end
if prompted LLM to check whether the given article and the corresponding summary belong to class incorrect_quantities then
Then output the class incorrect_quantities counter(variable) = counter(variable)+1 if counter(variable)==1 for a datapoint then stop checking for the next label for that datapoint
else
end else
end
if prompted LLM to check whether the given article and the correponding summary belongs to class false_attribution then
Then output the class false_attribution counter(variable) = counter(variable)+1 if counter(variable)==2 for a datapoint then stop checking for the next label for that datapoint
end
if prompted LLM to check whether the given article and the corresponding summary belong to class fabrication then
Then output the class fabrication counter(variable) = counter(variable)+1 if counter(variable)==2 for a datapoint then stop checking for the next label for that datapoint
Do not perform any action else end else else end else else end else end
Algorithm 3: Pseudocode
end
if prompted LLM to check whether the given article and the corresponding summary belong to class incorrect_quantities then
Then output the class incorrect_quantities counter(variable) = counter(variable)+1 if counter(variable)==2 for a datapoint then stop checking for the next label for that datapoint
else
end else
end
if prompted LLM to check whether the given article and the corresponding summary belong to class false_attribution then
Then output the class false_attribution counter(variable) = counter(variable)+1 if counter(variable)==2 for a datapoint then stop checking for the next label for that datapoint
Do not perform any action else end else else end else else end else end
Algorithm 4: Pseudocode
end
if prompted LLM to check whether the given article and the correponding summary belongs to class incorrect_quantities then
Then output the class incorrect_quantities counter(variable) = counter(variable)+1 if counter(variable)==2 for a datapoint then stop checking for the next label for that datapoint
( ) ← 0 ;
if prompted LLM to check whether the given article and the corresponding summary belong to class misrepresentation then
Then output the class misrepresentation counter(variable) = counter(variable)+1 if counter(variable)==4 for a datapoint then
stop checking for the next label for that datapoint else
end else
end
if prompted LLM to check whether the given article and the corresponding summary belong to class false_attribution then
Then output the class false_attribution counter(variable) = counter(variable)+1 if counter(variable)==4 for a datapoint then stop checking for the next label for that datapoint
end
if prompted LLM to check whether the given article and the corresponding summary belong to class fabrication then
Then output the class fabrication counter(variable) = counter(variable)+1 if counter(variable)==4 for a datapoint then stop checking for the next label for that datapoint
Do not perform any action else end else else end else else end else end
Algorithm 5: Pseudocode
end
if prompted LLM to check whether the given article and the corresponding summary belong to class incorrect_quantities then
Then output the class incorrect_quantities counter(variable) = counter(variable)+1 if counter(variable)==4 for a datapoint then stop checking for the next label for that datapoint
We were given the task of multi-label error classification where we had to classify a document into four classes namely misrepresentation, fabrication, false_attribution, and incorrect_quantities. We tried several prompt-based algorithmic approaches for the multi-label error classification task that we were given as a part of Task 2. We observed that the Algorithm 5 (Ensembling approach) that we explored obtained the best results. Future work would involve trying a few-shot technique and trying larger language models such as GPT-4.