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Fast and Accurate Factual Inconsistency Detection Over Long Documents

Barrett Martin Lattimer, Patrick Chen, Xinyuan Zhang, Yi Yang

blattimer@asapp.com

EMNLP 2023

https://arxiv.org/abs/2310.13189

Overview

Introducing SCALE, an reference-free NLI based factual inconsistency detection method, and ScreenEval, the longest dialogue based dataset for factual inconsistency detection presently available. Both can be found in our paper Fast and Accurate Factual Inconsistency Detection Over Long Documents.

SCALE uses a novel chunking strategy to achieve state-of-the-art factual inconsistency deteciton performance across many NLG domains, tasks, and over long documents (>6k tokens). SCALE's chunking approach enables fast relevant source text retrival over long documents.

SCALE

This metrics outputs the estimated probablility that a hypothesis is supported by a given premise SCALE(premise, hypothesis). Commonly the hypothesis is generated text and the premise is some ground truth text. For example, a premise may be a document and the hypothesis may be a language model generated summary sentence. The score is bounded as follows 0≤SCALE(premise, hypothesis)≤1. A higher score signifies a higher probability the hypothesis is factually consistent with the premise. A lower score signifies the hypothesis is more likely to be factually inconsistent with the premise. It is recommended to use Flan_T5_XL or Flan_T5_Large as the base model for the best results. Note: Using Flan_T5_Small as a base model will not result in accurate scores unless finetuned.

Install

To use the evaluation metric, first pip install the python module.

pip install scale-score

or install from source

pip install -e .

Score

Running the Metric

Import the score function and load your premises, hypothesies. For scoring, the premise is a list of entire document strings while the hypothesis are single sentences represented as is a list of list of strings. Each premise has a list of associated hypothesis with a one to one mapping based on index (premise_0 -> ['hypothesis_0_0', 'hypothesis_0_1'], premise_1-> ['hypothesis_1_0', 'hypothesis_1_1', 'hypothesis_1_2']).

from scale_score import score

premise = [
    'premise_0',
    'premise_1',
]
hypothesis = [
    ['hypothesis_0_0', 'hypothesis_0_1'],
    ['hypothesis_1_0', 'hypothesis_1_1', 'hypothesis_1_2']
]

results = score(premise, hypothesis)

Where the results correspond to each hypothesis scored with it's respecitve premise

results = [
    SCALE(premise_0, hypothesis_0_0), 
    SCALE(premise_0, hypothesis_0_1), 
    SCALE(premise_1, hypothesis_1_0), 
    SCALE(premise_1, hypothesis_1_1),
    SCALE(premise_1, hypothesis_1_2),
]

You can also use the scorer object to prevent loading the model at every call like so,

from scale_score.scorer import SCALEScorer
scorer = SCALEScorer(size='large', device='cuda')
results = scorer.score(premise, hypothesis)

Arguments

These arguments are the exact same for both score and scorer.score functions except scorer.score does not take in a size or device as that is set up when building the scorer object.

Evaluation

After scoring, use the evaluate_scale function to evaluate the results.

from scale_score.eval import evaluate_scale
from scale_score.scorer import SCALEScorer
scorer = SCALEScorer(size='small', device='cuda')
results = scorer.score(premise, hypothesis)
metrics = evaluate_scale(results)

The arguments for evaluate_scale are as follows

The metrics that are output are described below.

Retrieve

Running Retrieval

Import the retrieve function and load your premises, hypothesies.

NOTE: Premises are lists of lists in retrieval. Both premises and hypothesis are split down to the sentence or utterance level.

Each premise list has an associated hypothesis list with a one to one mapping based on index.

from scale_score import retrieve

premise = [
    ['premise_0_utt_0', 'premise_0_utt_1', 'premise_0_utt_2'],
    ['premise_1_utt_0', 'premise_1_utt_1'],
]
hypothesis = [
    ['hypothesis_0_0', 'hypothesis_0_1'],
    ['hypothesis_1_0', 'hypothesis_1_1', 'hypothesis_1_2']
]

results = retrieve(premise, hypothesis)

Where the results correspond to a list which has the most relevant premise utterance/sentence and the corresponding score.

You can also use the scorer object to prevent loading the model at every call like so,

from scale_score.scorer import SCALEScorer
scorer = SCALEScorer(size='small', device='cuda')
results = scorer.retrieve(premise, hypothesis)

Arguments

These arguments are the exact same for both retrieve and scorer.retrieve functions except scorer.retrieve does not take in a size or device as that is set up when building the scorer object.

ScreenEval

ScreenEval is located in the data folder stored as a json file. The following keys are important for the use of ScreenEval.