Truthfulqa Biogen Factuality Eval

---
name: truthfulqa-biogen-factuality-eval
description: Evaluates an LLM's factual accuracy and hallucination mitigation across multiple-choice, short-form, and long-form generation tasks. It measures the trade-off between truthfulness and informativeness, and quantifies the exact number of supported versus unsupported facts in generated text. Use when the user wants to benchmark on TruthfulQA, BioGEN, or asks about evaluating this task. Reports Accuracy, True*Info, FActScore.
metadata:
  skill_kind: dataset_eval
  source_arxiv: 2402.09267
  bibtex_key: [REDACTED]
  confidence: high
---

# truthfulqa-biogen-factuality-eval

> Self-Alignment for Factuality: Mitigating Hallucinations in LLMs via Self-Evaluation — Xiaoying Zhang et al. (2024) (arXiv:2402.09267, 2024)

## What this evaluates

Evaluates an LLM's factual accuracy and hallucination mitigation across multiple-choice, short-form, and long-form generation tasks. It measures the trade-off between truthfulness and informativeness, and quantifies the exact number of supported versus unsupported facts in generated text.

## Datasets

- **TruthfulQA** — total ?; splits: test (-1)
- **BioGEN** — total ?; splits: test (-1)

## Metrics

- `Accuracy` **(primary)** — range: percent
  - Percentage of correct multiple-choice answers selected by the model.
- `True*Info` **(primary)** — range: percent
  - Product of truthfulness (True) and informativeness (Info) scores, both evaluated via a fine-tuned GPT-3 classifier.
- `FActScore` **(primary)** — range: percent
  - Percentage of generated facts that are supported by external evidence, computed using the FActScore pipeline.
- `Respond ratio` — range: percent
  - Percentage of generated responses that are not refusals.
- `Fact counts (cor/incor)` — range: other
  - Absolute count of accurate (cor) and inaccurate (incor) facts per response, extracted via the FActScore methodology.

## Input / output format

**Input**: Dataset prompts with few-shot examples (6-shot for TruthfulQA MCQA and short-form generation; 5-shot for BioGEN long-form generation). Self-alignment methods use only the provided dataset prompts without external knowledge injection.

**Output**: Model-generated responses: multiple-choice answers for MCQA, short-form text for TruthfulQA generation, and long-form biographical text for BioGEN.

## Scoring recipe

```python
# TruthfulQA MC
accuracy = sum(pred == gold for pred, gold in mcqa_preds) / len(mcqa_preds)

# TruthfulQA Gen (evaluated via fine-tuned GPT-3)
true_score = gpt3_classifier.predict_truthfulness(gens)
info_score = gpt3_classifier.predict_informativeness(gens)
true_info = true_score * info_score

# BioGEN (evaluated via FActScore pipeline)
facts = extract_facts(gens)
cor = sum(1 for f in facts if f.supported)
incor = sum(1 for f in facts if not f.supported)
factscore = (cor / len(facts)) * 100 if facts else 0
respond_ratio = (sum(1 for g in gens if not g.is_refusal) / len(gens)) * 100
```

## Common pitfalls

- True*Info is computed as the product of True and Info percentages, not a separate classification score.
- FActScore and fact counts (cor/incor) rely on an external fact-checking pipeline (Tian et al. 2023a) rather than native model scoring.
- Baseline results on TruthfulQA are often cited from prior works rather than reproduced, which may cause metric inconsistencies if re-evaluated.

## Evidence (verbatim from paper)

> In evaluating performance on TruthfulQA, we report Accuracy for the MCQA task, alongside metrics of truthfulness (True), informativeness (Info), and a composite True∗Info score, all evaluated using a fine-tuned GPT-3 model*Lin et al. ([2022])*. For assessments on BioGEN, we present the FActScore percentage and the Respond ratio. Moreover, we quantify the correctness of generated content by reporting the number of accurate (cor) and inaccurate facts (incor) per response, following the methodology outlined by*Tian et al. ([2023a])*.

## Citation

```bibtex
@misc{zhang2024selfalignment,
  title={Self-Alignment for Factuality: Mitigating Hallucinations in LLMs via Self-Evaluation},
  author={Xiaoying Zhang et al. (2024)},
  year={2024},
  note={arXiv:2402.09267}
}
```

- arXiv: 2402.09267