--- title: "Evaluating Verifiability in Generative Search — Liu 2023" description: "Generative search engines answer queries with synthesized text that contains in-line citations, but it is unclear whether those citations actually back…" authors: ["Nelson Liu", "Tianyi Zhang", "Percy Liang"] year: 2023 venue: "Findings of the Association for Computational Linguistics: EMNLP 2023" source: "https://aclanthology.org/2023.findings-emnlp.467/" doi: "10.18653/v1/2023.findings-emnlp.467" licence: "CC BY 4.0 (ACL Anthology)" licence_url: "https://creativecommons.org/licenses/by/4.0/" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/en/papers/liu-2023-evaluating-verifiability-generative-search-engines/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/liu-2023-evaluating-verifiability-generative-search-engines/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2023](https://md.research.elamur.ai/free/en/papers/2023/page/2/) / Evaluating Verifiability in Generative Search Engines # Evaluating Verifiability in Generative Search Engines **Authors:** [Nelson Liu](https://md.research.elamur.ai/free/en/authors/nelson-liu/), [Tianyi Zhang](https://md.research.elamur.ai/free/en/authors/tianyi-zhang/), [Percy Liang](https://md.research.elamur.ai/free/en/authors/percy-liang/) · **Year:** 2023 · **Venue:** Findings of the Association for Computational Linguistics: EMNLP 2023 · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/) **Links:** [Publisher's page](https://aclanthology.org/2023.findings-emnlp.467/) · [PDF](https://aclanthology.org/2023.findings-emnlp.467.pdf) · [DOI 10.18653/v1/2023.findings-emnlp.467](https://doi.org/10.18653/v1/2023.findings-emnlp.467) · [OpenAlex](https://openalex.org/W4389518954) **Citations:** 137 citations (openalex-snapshot · 2026-10-14) · [157](#cited-by) in our library, of the 140673 papers whose bibliographies we read ## Summary Generative search engines answer queries with synthesized text that contains in-line citations, but it is unclear whether those citations actually back up the claims. The authors call this property verifiability and audit four commercial systems (Bing Chat, NeevaAI, perplexity.ai and YouChat) through human evaluation. The queries come from varied sources, including historical Google searches from NaturalQuestions and open-ended questions collected from Reddit. Annotators rated each response for fluency and perceived utility on Likert scales. They also checked sentence-level support to compute two proposed metrics, plus a combined citation F1: - **Citation recall:** the share of verification-worthy sentences that their citations fully back. - **Citation precision:** the share of citations that genuinely support their sentence. The answers read smoothly and look helpful. However, only about half of the sentences are fully supported, and roughly one in four citations fails to support its claim. Moreover, the systems judged most useful tend to cite least accurately. Because these tools already reach millions of users, the study exposes a real trust gap and supplies metrics and data for building more reliable systems. ## Key points - Average citation recall across the four engines is 51.5%, and average citation precision is 74.5%. - Perceived utility and citation precision are strongly negatively correlated across systems (r = −0.96). - Higher precision appears to come from copying or closely paraphrasing cited pages: BLEU and BERTScore similarity to the cited evidence correlates with precision at r = 0.80. - Statements are evaluated at the sentence level. Partially supporting citations count toward precision only in restricted cases, and finer-grained claim splitting is left for future work. - 34 annotators carried out the evaluation, and the annotations are released at github.com/nelson-liu/evaluating-verifiability-in-generative-search-engines. *Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.* Fields: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/) ## Cited by Cited by 157 papers of our library cite it (of the 140673 whose bibliographies we read); 10 of them have a page here, 147 not yet. - [Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection](https://md.research.elamur.ai/free/en/papers/asai-2023-self-rag-learning-retrieve-generate/) - [Benchmarking Large Language Models in Retrieval-Augmented Generation](https://md.research.elamur.ai/free/en/papers/chen-2023-benchmarking-large-language-models-retrieval/) - [Enabling Large Language Models to Generate Text with Citations](https://md.research.elamur.ai/free/en/papers/gao-2023-enabling-large-language-models-generate/) - [Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection](https://md.research.elamur.ai/free/en/papers/greshake-2023-ve-signed-up-compromising-real/) - [Evaluating Large Language Models for Health-related Queries with Presuppositions](https://md.research.elamur.ai/free/en/papers/kaur-2024-evaluating-large-language-models-health/) - [FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation](https://md.research.elamur.ai/free/en/papers/min-2023-factscore-fine-grained-atomic-evaluation/) - [PragmatiCQA: A Dataset for Pragmatic Question Answering in Conversations](https://md.research.elamur.ai/free/en/papers/qi-2023-pragmaticqa-dataset-pragmatic-question-answering/) - [MiniCheck: Efficient Fact-Checking of LLMs on Grounding Documents](https://md.research.elamur.ai/free/en/papers/tang-2024-minicheck-efficient-fact-checking-llms/) - [FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation](https://md.research.elamur.ai/free/en/papers/vu-2023-freshllms-refreshing-large-language-models/) - [How Language Model Hallucinations Can Snowball](https://md.research.elamur.ai/free/en/papers/zhang-2023-language-model-hallucinations-snowball/) ## Cite The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{liu2023evaluating, title = {{Evaluating Verifiability in Generative Search Engines}}, author = {Liu, Nelson and Zhang, Tianyi and Liang, Percy}, booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2023}, year = {2023}, doi = {10.18653/v1/2023.findings-emnlp.467}, url = {https://doi.org/10.18653/v1/2023.findings-emnlp.467} } ``` - [← previous Evaluating the Logical Reasoning Ability of ChatGPT and GPT-4](https://md.research.elamur.ai/free/en/papers/liu-2023-evaluating-logical-reasoning-ability-chatgpt/) - [next → Exclaiming non-sententially: exclamative sluices and the (non-elliptical) nature of ellipsis constructions](https://md.research.elamur.ai/free/en/papers/ginzburg-2023-exclaiming-non-sententially-exclamative-sluices/) - [↑ back to papers of 2023](https://md.research.elamur.ai/free/en/papers/2023/page/2/) ## References 26 references, 12 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. 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