HARPA Summary

The source paper is "Modeling Information Change in Science Communication with Semantically Matched Paraphrases" (16 citations, 2022, ID: 0b7cc0e510ef05ad394a36d9cee9ddf5f2ae912f). This idea builds on a progression of related work [9de9fa60a786ca23f924f5521326b2a264c22228, 61a3d27beacc14303d3500ac647122fd32329b35, cfce709a65f90312d2bdc1a6cf0380c19becf694, 6f75e8b61f13562237851d8119cb2f9d49e073fb, 52269391f48885ad440809962f27b9022d3ee4ba, 0133c1128f2036ecb6b65ab15c562b71bf4f18a0, 9f424c20deca24f854ea2e8484b00aef888037ca].

The analysis reveals a progression from the source paper's focus on paraphrase detection and fact-checking to broader themes of misinformation and hallucination detection in LLMs. Papers 2, 3, and 4 specifically address hallucinations, a form of misinformation, building upon the source paper's findings. Papers 5 and 6 extend the evaluation of LLM outputs in contextual and dynamic scenarios. A research idea that advances the field could focus on developing a comprehensive framework for detecting both paraphrase-based misinformation and hallucinations in LLMs, leveraging insights from the SPICED dataset and recent advancements in hallucination detection models.

Hypothesis

Integrating ensemble methods with logit-based probability scoring will improve the precision and recall of misinformation and hallucination detection in large language models within healthcare and finance domains.

Research Gap

Existing research has not extensively explored the combination of ensemble methods with logit-based probability scoring for misinformation and hallucination detection in healthcare and finance domains. This combination could potentially enhance detection accuracy by leveraging the strengths of both approaches in a novel way.

Hypothesis Elements

Independent variable: Integration of ensemble methods with logit-based probability scoring

Dependent variable: Precision and recall of misinformation and hallucination detection

Comparison groups: Three approaches: Individual detection models (Baseline 1), Simple ensemble method (Baseline 2), and Integrated approach (Experimental)

Baseline/control: Individual detection models (FactCC, SummaC, and SelfCheckGPT) and simple ensemble method using majority voting

Assumptions: Ensemble methods reduce false positives and negatives; logit-based scoring provides precise measure of confidence in predictions

Measurement method: Precision, recall, F1 Score, and ROC-AUC metrics evaluated on domain-specific datasets (MedQA and FinancialQA)

Overview

This research explores the integration of ensemble methods with logit-based probability scoring to enhance the detection of misinformation and hallucinations in large language models, specifically within the healthcare and finance domains. Ensemble methods, which aggregate predictions from multiple models, are known for their robustness and ability to reduce false positives and negatives. Logit-based probability scoring, on the other hand, provides a quantitative measure of content reliability by analyzing the distribution of logits from language models. By combining these two approaches, the research aims to leverage the strengths of ensemble methods in handling variability and complexity with the precise token-level analysis provided by logit-based scoring. This integration is expected to improve both precision and recall metrics, crucial for domains where accuracy is paramount. The hypothesis will be tested using datasets specific to healthcare and finance, such as medical QA pairs and financial reports, ensuring that the models are evaluated in contexts where misinformation and hallucination detection is critical. The expected outcome is an improved detection system that balances precision and recall, reducing the risk of false positives and negatives while maintaining high accuracy in identifying true misinformation and hallucinations.

Background

Ensemble Methods: Ensemble methods involve combining multiple detection models to enhance robustness and reduce false positives and negatives. This approach aggregates predictions from different models like FactSumm, Smart, SummaC, and Selfcheckgpt. Implementation typically involves running each model independently on the same input data and then using a voting mechanism or weighted averaging to determine the final prediction. This strategy can be particularly effective in domains requiring high accuracy, such as healthcare and finance, where the cost of misinformation is significant. The ensemble method may also involve hyperparameter tuning to optimize the contribution of each model to the final decision.

Logit-Based Probability Scoring: Logit-based probability scoring utilizes the logit outputs from language models to assess the accuracy of specific tokens or phrases. This method involves analyzing the distribution of logits to determine the trustworthiness and consistency of generated text. In practice, this could involve setting a threshold for logit scores, below which content is flagged as potentially hallucinated or misinformative. This approach can be integrated into existing detection models as an additional layer of verification, providing a quantitative measure of content reliability. It is particularly useful in scenarios where precise token-level analysis is required, such as in legal or medical documents.

Implementation

The hypothesis will be implemented by first setting up an ensemble framework that integrates multiple misinformation and hallucination detection models, such as FactSumm, Smart, SummaC, and Selfcheckgpt. Each model will independently process the same input data, and their predictions will be aggregated using a voting mechanism or weighted averaging to produce a final decision. Simultaneously, logit-based probability scoring will be applied to the outputs of these models. The logits from each model will be analyzed to assess the confidence of each prediction, with a threshold set to flag potentially unreliable content. This dual-layer approach will be tested on domain-specific datasets, such as medical QA pairs and financial reports, to evaluate its effectiveness in improving precision and recall metrics. The integration logic will involve a pipeline where the ensemble method provides an initial prediction, which is then refined by the logit-based scoring to ensure high confidence in the final output. The expected outcome is a more accurate detection system that reduces false positives and negatives while maintaining high precision and recall.

Operationalization Information

Please implement an experiment to test whether integrating ensemble methods with logit-based probability scoring improves misinformation and hallucination detection in large language models, specifically for healthcare and finance domains.

Experiment Overview

This experiment will compare three approaches to misinformation detection:
1. Baseline 1: Individual detection models (FactCC, SummaC, and SelfCheckGPT) used separately
2. Baseline 2: A simple ensemble method that uses majority voting from the individual models
3. Experimental: An integrated approach that combines ensemble methods with logit-based probability scoring

Pilot Settings

Implement a global variable PILOT_MODE with three possible settings: MINI_PILOT, PILOT, or FULL_EXPERIMENT.

MINI_PILOT: Use only 10 examples from each domain (healthcare and finance) for quick debugging. Run each detection model on these examples and evaluate the results.
PILOT: Use 100 examples from each domain. This should be sufficient to see if there are promising differences between the baseline and experimental approaches.
FULL_EXPERIMENT: Use the complete datasets for both domains, with proper train/validation/test splits.

Start by running the MINI_PILOT. If everything looks good, proceed to the PILOT. After the PILOT completes, stop and do not run the FULL_EXPERIMENT (a human will manually verify the results and make the change to FULL_EXPERIMENT if appropriate).

Datasets

Healthcare Dataset: Use the MedQA dataset for medical question-answer pairs. For the MINI_PILOT, select 10 QA pairs. For the PILOT, use 100 QA pairs. For the FULL_EXPERIMENT, use the complete dataset with proper splits.
Financial Dataset: Use the FinancialQA dataset containing financial statements and reports. Apply the same sampling strategy as for the healthcare dataset.

For both datasets, create a ground truth annotation of whether each answer contains misinformation or hallucinations. If these annotations don't exist, you'll need to create them using a combination of expert knowledge and model-based verification.

Implementation Details

1. Individual Detection Models (Baseline 1)

Implement or adapt the following detection models:
- FactCC: A model that checks factual consistency between source and generated text
- SummaC: A model that evaluates consistency in summarization
- SelfCheckGPT: A model that uses self-consistency to detect hallucinations

Each model should output a binary classification (misinformation/hallucination detected or not) and a confidence score for each input.

2. Simple Ensemble Method (Baseline 2)

Implement a majority voting system that aggregates the binary classifications from the individual models. If at least two out of three models flag content as misinformation/hallucination, the ensemble flags it as well.

3. Integrated Approach (Experimental)

Implement the integrated approach with these components:

Ensemble Framework

Create a framework that:
- Runs all three detection models on the same input
- Collects both binary classifications and confidence scores
- Implements weighted voting based on model confidence
- Outputs an initial classification and confidence score

Logit-Based Scoring Module

Create a module that:
- Extracts logit distributions from the language models for each token in the generated text
- Analyzes the distribution to identify tokens with low confidence (potential hallucinations)
- Sets a threshold for flagging content based on logit scores
- Refines the ensemble's initial classification using logit-based evidence

Integration Logic

Implement a pipeline where:
1. The ensemble framework provides an initial classification
2. The logit-based scoring module refines this classification
3. The final decision incorporates both ensemble consensus and token-level confidence

Evaluation

Evaluate all three approaches (Individual Models, Simple Ensemble, Integrated Approach) using:

Precision: The proportion of true positive identifications out of all positive identifications
Recall: The proportion of true positive identifications out of all actual positives
F1 Score: The harmonic mean of precision and recall
ROC-AUC: The area under the receiver operating characteristic curve

Report these metrics separately for healthcare and finance domains, and also as an overall average.

Statistical Analysis

Perform statistical significance testing to determine if the differences between approaches are statistically significant:

Use bootstrap resampling to generate confidence intervals for each metric
Perform paired t-tests or Wilcoxon signed-rank tests to compare approaches
Report p-values and effect sizes

Output and Visualization

Generate the following outputs:

Tables showing precision, recall, F1, and ROC-AUC for each approach across domains
Confusion matrices for each approach
ROC curves comparing the three approaches
Precision-Recall curves comparing the three approaches
Examples of correctly and incorrectly classified instances for each approach

Implementation Notes

Use numpy for numerical operations and pandas for data manipulation
Implement proper logging to track experiment progress and results
Save intermediate results to allow for resuming experiments if needed
Use a random seed for reproducibility
Document all hyperparameters and design choices

Please run the MINI_PILOT first, then if everything looks good, proceed to the PILOT. After the PILOT completes, stop and await human verification before running the FULL_EXPERIMENT.

Paper ID

Motivation