Update README.md

Author: alopatenko

README.md

@@ -208,6 +208,7 @@ My view on LLM Evaluation: [Deck](LLMEvaluation.pdf), and [SF Big Analytics and
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 ### Question answering
 QA is used in many vertical domains, see Vertical section bellow
+- Search Engines in an AI Era: The False Promise of Factual and Verifiable Source-Cited Responses, Oct 2024, Salesforce, [arxiv](https://arxiv.org/abs/2410.22349) [Answer Engine (RAG) Evaluation Repository](https://github.com/SalesforceAIResearch/answer-engine-eval)
 - Introducing SimpleQA, OpenAI, Oct 2024 [OpenAI](https://openai.com/index/introducing-simpleqa/)
 - CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge, Jun 2019, [ACL](https://aclanthology.org/N19-1421/) 
 - Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering, Sep 2018, [arxiv](https://arxiv.org/abs/1809.02789) [OpenBookQA dataset at AllenAI](https://allenai.org/data/open-book-qa)
@@ -404,6 +405,7 @@ QA is used in many vertical domains, see Vertical section bellow
 ## LLM Systems
 ### RAG Evaluation
 - Google Frames Dataset for evaluation of RAG systems, Sep 2024, [arxiv paper: Fact, Fetch, and Reason: A Unified Evaluation of Retrieval-Augmented Generation
+- Search Engines in an AI Era: The False Promise of Factual and Verifiable Source-Cited Responses, Oct 2024, Salesforce, [arxiv](https://arxiv.org/abs/2410.22349) [Answer Engine (RAG) Evaluation Repository](https://github.com/SalesforceAIResearch/answer-engine-eval)
 ](https://arxiv.org/abs/2409.12941) [Hugging Face, dataset](https://huggingface.co/datasets/google/frames-benchmark)
 - RAGAS: Automated Evaluation of Retrieval Augmented Generation Jul 23, [arxiv](https://arxiv.org/abs/2309.15217)
 - ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systems Nov 23, [arxiv](https://arxiv.org/abs/2311.09476)
@@ -431,6 +433,7 @@ And Dialog systems
 - Is ChatGPT a Good Recommender? A Preliminary Study Apr 2023 [arxiv](https://arxiv.org/abs/2304.10149)
 - IRSC: A Zero-shot Evaluation Benchmark for Information Retrieval through Semantic Comprehension in Retrieval-Augmented Generation Scenarios, Sep 2024, [arxiv](https://arxiv.org/abs/2409.15763)
 - LaMP: When Large Language Models Meet Personalization, Apr 2023,  [arxiv](https://arxiv.org/abs/2304.11406)
+- Search Engines in an AI Era: The False Promise of Factual and Verifiable Source-Cited Responses, Oct 2024, Salesforce, [arxiv](https://arxiv.org/abs/2410.22349) [Answer Engine (RAG) Evaluation Repository](https://github.com/SalesforceAIResearch/answer-engine-eval)
 - BIRCO: A Benchmark of Information Retrieval Tasks with Complex Objectives, Feb 2024, [arxiv](https://arxiv.org/abs/2402.14151)
 - Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agents, Apr 2023, [arxiv](https://arxiv.org/abs/2304.09542)
 -  BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models, Oct 2021, [arxiv](https://arxiv.org/abs/2104.08663)