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# Question Which public figures are repeatedly mentioned across various entertainment articles? # Graph Overview of Prominent Public Figures in Entertainment # RAG The entertainment industry is vast and diverse, encompassing film, television, music, sports, and digital media. Certain public figures stand out due to their significant contributions and influence across these sectors. The following summary highlights key individuals who are repeatedly mentioned in various entertainment articles, reflecting their impact and presence within the industry. Actors and Directors [...] Public Figures in Controversy [...] Musicians and Executives [...] Athletes and Coaches [...] Influencers and Entrepreneurs [...] The repeated mention of these figures in entertainment articles signifies their ongoing relevance and the public’s interest in their work. Their influence spans across various aspects of entertainment, from shaping cultural narratives in film and television to driving trends in music and digital media. These individuals not only contribute to their respective fields but also influence the broader cultural landscape, often becoming central figures in social discussions and public discourse. # Naïve Public figures who are repeatedly mentioned across various entertainment articles include Taylor Swift, Travis Kelce, Britney Spears, and Justin Timberlake. These individuals have been highlighted for various reasons, ranging from their professional achievements to their personal lives. Taylor Swift [...] Travis Kelce [...] Britney Spears [...] Justin Timberlake [...] These figures are frequently covered due to their high-profile status and the public’s interest in their careers and personal lives. Their activities, whether in music, sports, or personal relationships, have significant cultural and economic impacts, as evidenced by the media coverage and public reactions. # LLM Comprehensiveness: Winner=1 (Graph RAG) Decision Answer 1 is better because it provides a more comprehensive and detailed list of public figures from a wider range of entertainment sectors, including film, television, music, sports, gaming, and digital media. It also includes specific examples of their contributions and the impact they have on their respective fields, as well as mentions of controversies and their implications. Answer 2, while detailed in its coverage of a few individuals, is limited to a smaller number of public figures and focuses primarily on their personal lives and relationships rather than a broad spectrum of their professional influence across the entertainment industry. Diversity: Winner=1 (Graph RAG) Answer 1 is better because it provides a more varied and rich response by covering a wide range of public figures from different sectors of the entertainment industry, including film, television, music, sports, gaming, and digital media. It offers insights into the contributions and influence of these figures, as well as controversies and their impact on public discourse.

Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {q2:, a2:}}, {7B: {q1:, a1:}, {q2:, a2:}}

The answer also cites specific data sources for each mentioned figure, indicating a diverse range of evidence to support the claims. In contrast, Answer 2 focuses on a smaller group of public figures, primarily from the music industry and sports, and relies heavily on a single source for data, which makes it less diverse in perspectives and insights. Empowerment: Winner=1 (Graph RAG) Answer 1 is better because it provides a comprehensive and structured overview of public figures across various sectors of the entertainment industry, including film, television, music, sports, and digital media. It lists multiple individuals, providing specific examples of their contributions and the context in which they are mentioned in entertainment articles, along with references to data reports for each claim.

Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {q2:, a2:}}, {7B: {q1:, a1:}, {q2:, a2:}}

This approach helps the reader understand the breadth of the topic and make informed judgments without being misled. In contrast, Answer 2 focuses on a smaller group of public figures and primarily discusses their personal lives and relationships, which may not provide as broad an understanding of the topic. While Answer 2 also cites sources, it does not match the depth and variety of Answer 1. Directness: Winner=2 (Naïve RAG) Answer 2 is better because it directly lists specific public figures who are repeatedly mentioned across various entertainment articles, such as Taylor Swift, Travis Kelce, Britney Spears, and Justin Timberlake, and provides concise explanations for their frequent mentions. Answer 1, while comprehensive, includes a lot of detailed information about various figures in different sectors of entertainment, which, while informative, does not directly answer the question with the same level of conciseness and specificity as Answer 2. Example question for pe News article dataset, wip generated answers from Graph RAG (C2) and Naïve RAG, as well as LLM-generated assessments. 8

Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {q2:, a2:}}, {7B: {q1:, a1:}, {q2:, a2:}}

| |SS|TS|C0|C1|C2|C3| |---|---|---|---|---|---|---| |SS|50|17|28|25|22|21| |TS|83|50|50|48|43|44| |C0|72|50|50|53|50|49| |C1|75|52|47|50|52|50| |C2|78|57|50|48|50|52| |C3|79|56|51|50|48|50| | |SS|TS|C0|C1|C2|C3| |---|---|---|---|---|---|---| |SS|50|20|28|25|21|21| |TS|80|50|44|41|38|36| |C0|72|56|50|52|54|52| |C1|75|59|48|50|58|55| |C2|79|62|46|42|50|59| |C3|79|64|48|45|41|50| Figure 4: Head-to-head win rate percentages of (row condition) over (column condition) across two datasets, four metrics, and 125 questions per comparison (each repeated five times and averaged). The overall winner per dataset and metric is shown in bold. Self-win rates were not computed but are shown as the expected 50% for reference. All Graph RAG conditions outperformed na¨ıve RAG on comprehensiveness and diversity. Conditions C1-C3 also showed slight improvements in answer comprehensiveness and diversity over TS (global text summarization without a graph index). # 3.5 Configuration The effect of context window size on any particular task is unclear, especially for models like gpt-4-turbo with a large context size of 128k tokens. Given the potential for information to be “lost in the middle” of longer contexts (Kuratov et al., 2024; Liu et al., 2023), we wanted to explore the effects of varying the context window size for our combinations of datasets, questions, and metrics. In particular, our goal was to determine the optimum context size for our baseline condition (SS) and then use this uniformly for all query-time LLM use. To that end, we tested four context window sizes: 8k, 16k, 32k and 64k. Surprisingly, the smallest context window size tested (8k) was universally better for all comparisons on comprehensiveness (average win rate of 58.1%), while performing comparably with larger context sizes on diversity (average win rate = 52.4%), and empowerment (average win rate = 51.3%). Given our preference for more comprehensive and diverse answers, we therefore used a fixed context window size of 8k tokens for the final evaluation. # 3.6 Results The indexing process resulted in a graph consisting of 8564 nodes and 20691 edges for the Podcast dataset, and a larger graph of 15754 nodes and 19520 edges for the News dataset. Table 3 shows the number of community summaries at different levels of each graph community hierarchy. Global approaches vs.

Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {q2:, a2:}}, {7B: {q1:, a1:}, {q2:, a2:}}

na¨ıve RAG. As shown in Figure 4, global approaches consistently outperformed the na¨ıve RAG (SS) approach in both comprehensiveness and diversity metrics across datasets. Specifically, global approaches achieved comprehensiveness win rates between 72-83% for Podcast transcripts and 72-80% for News articles, while diversity win rates ranged from 75-82% and 62-71% respectively. Our use of directness as a validity test also achieved the expected results, i.e., that na¨ıve RAG produces the most direct responses across all comparisons.

Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {q2:, a2:}}, {7B: {q1:, a1:}, {q2:, a2:}}

| |C0|C1|C2|C3|TS| |---|---|---|---|---|---| |Units|34|367|969|1310|1669| |Tokens|26657|225756|565720|746100|1014611| |% Max|2.6|22.2|55.8|73.5|100| Table 3: Number of context units (community summaries for C0-C3 and text chunks for TS), corresponding token counts, and percentage of the maximum token count. Map-reduce summarization of source texts is the most resource-intensive approach requiring the highest number of context tokens. Root-level community summaries (C0) require dramatically fewer tokens per query (9x-43x). Community summaries vs.

Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {q2:, a2:}}, {7B: {q1:, a1:}, {q2:, a2:}}

source texts. When comparing community summaries to source texts using Graph RAG, community summaries generally provided a small but consistent improvement in answer comprehensiveness and diversity, except for root-level summaries. Intermediate-level summaries in the Podcast dataset and low-level community summaries in the News dataset achieved comprehensiveness win rates of 57% and 64%, respectively. Diversity win rates were 57% for Podcast intermediate-level summaries and 60% for News low-level community summaries. Table 3 also illustrates the scalability advantages of Graph RAG compared to source text summarization: for low-level community summaries (C3), Graph RAG required 26-33% fewer context tokens, while for root-level community summaries (C0), it required over 97% fewer tokens. For a modest drop in performance compared with other global methods, root-level Graph RAG offers a highly efficient method for the iterative question answering that characterizes sensemaking activity, while retaining advantages in comprehensiveness (72% win rate) and diversity (62% win rate) over na¨ıve RAG. Empowerment.

Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {q2:, a2:}}, {7B: {q1:, a1:}, {q2:, a2:}}

Empowerment comparisons showed mixed results for both global approaches versus na¨ıve RAG (SS) and Graph RAG approaches versus source text summarization (TS). Ad-hoc LLM use to analyze LLM reasoning for this measure indicated that the ability to provide specific examples, quotes, and citations was judged to be key to helping users reach an informed understanding. Tuning element extraction prompts may help to retain more of these details in the Graph RAG index. # Related Work # RAG Approaches and Systems When using LLMs, RAG involves first retrieving relevant information from external data sources, then adding this information to the context window of the LLM along with the original query (Ram et al., 2023). Na¨ıve RAG approaches (Gao et al., 2023) do this by converting documents to text, splitting text into chunks, and embedding these chunks into a vector space in which similar positions represent similar semantics. Queries are then embedded into the same vector space, with the text chunks of the nearest k vectors used as context. More advanced variations exist, but all solve the problem of what to do when an external dataset of interest exceeds the LLM’s context window. Advanced RAG systems include pre-retrieval, retrieval, post-retrieval strategies designed to overcome the drawbacks of Na¨ıve RAG, while Modular RAG systems include patterns for iterative and dynamic cycles of interleaved retrieval and generation (Gao et al., 2023). Our implementation of Graph RAG incorporates multiple concepts related to other systems. For example, our community summaries are a kind of self-memory (Selfmem, Cheng et al., 2024) for generation-augmented retrieval (GAR, Mao et al., 2020) that facilitates future generation cycles, while our parallel generation of community answers from these summaries is a kind of iterative (Iter-RetGen, Shao et al., 2023) or federated (FeB4RAG, Wang et al., 2024) retrieval-generation strategy. Other systems have also combined these concepts for multi-document summarization (CAiRE-COVID, Su et al., 2020) and multi-hop question answering (ITRG, Feng et al., 2023; IR-CoT, Trivedi et al., 2022; DSP, Khattab et al., 2022). Our use of a hierarchical index and summarization also bears resemblance to further approaches, such as generating a hierarchical index of text chunks by clustering the vectors of text embeddings (RAPTOR, Sarthi et al., 2024) or generating a “tree of clarifications” to answer multiple interpretations of ambiguous questions (Kim et al., 2023). However, none of these iterative or hierarchical approaches use the kind of self-generated graph index that enables Graph RAG.

Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {q2:, a2:}}, {7B: {q1:, a1:}, {q2:, a2:}}

# Graphs and LLMs Use of graphs in connection with LLMs and RAG is a developing research area, with multiple directions already established. These include using LLMs for knowledge graph creation (Trajanoska et al., 2023) and completion (Yao et al., 2023), as well as for the extraction of causal graphs (Ban et al., 2023; Zhang et al., 2024) from source texts. They also include forms of advanced RAG (Gao et al., 2023) where the index is a knowledge graph (KAPING, Baek et al., 2023), where subsets of the graph structure (G-Retriever, He et al., 2024) or derived graph metrics (Graph-ToolFormer, Zhang, 2023) are the objects of enquiry, where narrative outputs are strongly grounded in the facts of retrieved subgraphs (SURGE, Kang et al., 2023), where retrieved event-plot subgraphs are serialized using narrative templates (FABULA, Ranade and Joshi, 2023), and where the system supports both creation and traversal of text-relationship graphs for multi-hop question answering (Wang et al., 2023b). In terms of open-source software, a variety a graph databases are supported by both the LangChain (LangChain, 2024) and LlamaIndex (LlamaIndex, 2024) libraries, while a more general class of graph-based RAG applications is also emerging, including systems that can create and reason over knowledge graphs in both Neo4J (NaLLM, Neo4J, 2024) and Nebula-Graph (GraphRAG, NebulaGraph, 2024) formats. Unlike our Graph RAG approach, however, none of these systems use the natural modularity of graphs to partition data for global summarization. # Discussion Limitations of evaluation approach.

Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {q2:, a2:}}, {7B: {q1:, a1:}, {q2:, a2:}}

Our evaluation to date has only examined a certain class of sensemaking questions for two corpora in the region of 1 million tokens. More work is needed to understand how performance varies across different ranges of question types, data types, and dataset sizes, as well as to validate our sensemaking questions and target metrics with end users. Comparison of fabrication rates, e.g., using approaches like SelfCheckGPT (Manakul et al., 2023), would also improve on the current analysis. Trade-offs of building a graph index. We consistently observed Graph RAG achieve the best head-to-head results against other methods, but in many cases the graph-free approach to global summarization of source texts performed competitively. The real-world decision about whether to invest in building a graph index depends on multiple factors, including the compute budget, expected number of lifetime queries per dataset, and value obtained from other aspects of the graph index (including the generic community summaries and the use of other graph-related RAG approaches). Future work. The graph index, rich text annotations, and hierarchical community structure supporting the current Graph RAG approach offer many possibilities for refinement and adaptation. This includes RAG approaches that operate in a more local manner, via embedding-based matching of user queries and graph annotations, as well as the possibility of hybrid RAG schemes that combine embedding-based matching against community reports before employing our map-reduce summarization mechanisms. This “roll-up” operation could also be extended across more levels of the community hierarchy, as well as implemented as a more exploratory “drill down” mechanism that follows the information scent contained in higher-level community summaries. # Conclusion We have presented a global approach to Graph RAG, combining knowledge graph generation, retrieval-augmented generation (RAG), and query-focused summarization (QFS) to support human sensemaking over entire text corpora. Initial evaluations show substantial improvements over a naïve RAG baseline for both the comprehensiveness and diversity of answers, as well as favorable comparisons to a global but graph-free approach using map-reduce source text summarization. For situations requiring many global queries over the same dataset, summaries of root-level communities in the entity-based graph index provide a data index that is both superior to naïve RAG and achieves competitive performance to other global methods at a fraction of the token cost. An open-source, Python-based implementation of both global and local Graph RAG approaches is forthcoming at https://aka.ms/graphrag.

Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {q2:, a2:}}, {7B: {q1:, a1:}, {q2:, a2:}}

IEEE. IEEE standard for information technology–telecommunications and information exchange between systems - local and Metropolitan Area Networks–specific requirements - part 11: Wireless LAN medium access control (MAC) and physical layer (PHY) specifications. IEEE Sp 802.11-2020 (Revision of IEEE Sp 802.11-2016), pp. 1–4379, 2021. doi: 10.1109/IEEESTD. 2021.9363693.

Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {q2:, a2:}}, {7B: {q1:, a1:}, {q2:, a2:}}

# Acknowledgements We would also like to thank the following people who contributed to the work: Alonso Guevara Fernández, Amber Hoak, Andrés Morales Esquivel, Ben Cutler, Billie Rinaldi, Chris Sanchez, Chris Trevino, Christine Caggiano, David Tittsworth, Dayenne de Souza, Douglas Orbaker, Ed Clark, Gabriel Nieves-Ponce, Gaudy Blanco Meneses, Kate Lytvynets, Katy Smith, Mónica Carvajal, Nathan Evans, Richard Ortega, Rodrigo Racanicci, Sarah Smith, and Shane Solomon. # References Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., et al. (2023). Gpt-4 technical report. arXiv preprint arXiv:2303.08774. Anil, R., Borgeaud, S., Wu, Y., Alayrac, J.-B., Yu, J., Soricut, R., Schalkwyk, J., Dai, A. M., Hauth, A., et al. (2023). Gemini: a family of highly capable multimodal models. arXiv preprint arXiv:2312.11805. Baek, J., Aji, A. F., and Saffari, A.

Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {q2:, a2:}}, {7B: {q1:, a1:}, {q2:, a2:}}

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Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {q2:, a2:}}, {7B: {q1:, a1:}, {q2:, a2:}}

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Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {q2:, a2:}}, {7B: {q1:, a1:}, {q2:, a2:}}

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Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {q2:, a2:}}, {7B: {q1:, a1:}, {q2:, a2:}}

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Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {q2:, a2:}}, {7B: {q1:, a1:}, {q2:, a2:}}

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Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {q2:, a2:}}, {7B: {q1:, a1:}, {q2:, a2:}}

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Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {q2:, a2:}}, {7B: {q1:, a1:}, {q2:, a2:}}

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Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {q2:, a2:}}, {7B: {q1:, a1:}, {q2:, a2:}}

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Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {q2:, a2:}}, {7B: {q1:, a1:}, {q2:, a2:}}

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Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {q2:, a2:}}, {7B: {q1:, a1:}, {q2:, a2:}}

Nils Reimers and Iryna Gurevych. Sentence-BERT: Sentence embeddings using siamese bert-networks. In Proceedings of pe 2019 Conference on Empirical Mepods in Natural Language Processing and pe 9p International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, 2019. Sumit Soman and Ranjani HG. Observations on LLMs for telecom domain: Capabilities and limitations (To appear in pe proceedings of The Third International Conference on Artificial Intelligence and Machine Learning Systems). arXiv preprint arXiv:2305.13102, 2023. Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, and Tie-Yan Liu. MPNET: Masked and permuted pre-training for language understanding. Advances in Neural Information Processing Systems, 33: 16857–16867, 2020. Sabrina Toro, Anna V Anagnostopoulos, Sue Bello, Kai Blumberg, Rhiannon Cameron, Leigh Carmody, Alexander D Diehl, Damion Dooley, William Duncan, Petra Fey, et al. Dynamic retrieval augmented generation of ontologies using artificial intelligence (DRAGON-AI). arXiv preprint arXiv:2312.10904, 2023. Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288, 2023. Benfeng Xu, Chunxu Zhao, Wenbin Jiang, Pengfei Zhu, Songtai Dai, Chao Pang, Zhuo Sun, Shuohuan Wang, and Yu Sun. Retrieval-augmented domain adaptation of language models. In Proceedings of pe 8p Workshop on Representation Learning for NLP (RepL4NLP 2023), pp. 54–64, 2023.

Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {q2:, a2:}}, {7B: {q1:, a1:}, {q2:, a2:}}

# NebulaGraph (2024). Nebulagraph launches industry-first graph rag: Retrieval-augmented generation with llm based on knowledge graphs. https://www.nebula-graph.io/posts/graph-RAG # Neo4J (2024). Project NaLLM. https://github.com/neo4j/NaLLM # Newman, M. E. (2006). Modularity and community structure in networks. Proceedings of the national academy of sciences, 103(23):8577–8582. # Ram, O., Levine, Y., Dalmedigos, I., Muhlgay, D., Shashua, A., Leyton-Brown, K., and Shoham, Y. (2023). In-context retrieval-augmented language models. Transactions of the Association for Computational Linguistics, 11:1316–1331. # Ranade, P. and Joshi, A. (2023). Fabula: Intelligence report generation using retrieval-augmented narrative construction. arXiv preprint arXiv:2310.13848. # Sarthi, P., Abdullah, S., Tuli, A., Khanna, S., Goldie, A., and Manning, C. D. (2024). Raptor: Recursive abstractive processing for tree-organized retrieval. arXiv preprint arXiv:2401.18059.

Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {q2:, a2:}}, {7B: {q1:, a1:}, {q2:, a2:}}

# Scott, K. (2024). Behind the Tech. https://www.microsoft.com/en-us/behind-the-tech # Shao, Z., Gong, Y., Shen, Y., Huang, M., Duan, N., and Chen, W. (2023). Enhancing retrieval-augmented large language models with iterative retrieval-generation synergy. arXiv preprint arXiv:2305.15294. # Su, D., Xu, Y., Yu, T., Siddique, F. B., Barezi, E. J., and Fung, P. (2020). Caire-covid: A question answering and query-focused multi-document summarization system for covid-19 scholarly information management. arXiv preprint arXiv:2005.03975. # Tang, Y. and Yang, Y.

Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {q2:, a2:}}, {7B: {q1:, a1:}, {q2:, a2:}}

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Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {q2:, a2:}}, {7B: {q1:, a1:}, {q2:, a2:}}

# Trivedi, H., Balasubramanian, N., Khot, T., and Sabharwal, A. (2022). Interleaving retrieval with chain-of-thought reasoning for knowledge-intensive multi-step questions. arXiv preprint arXiv:2212.10509. # Wang, J., Liang, Y., Meng, F., Sun, Z., Shi, H., Li, Z., Xu, J., Qu, J., and Zhou, J.

Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {q2:, a2:}}, {7B: {q1:, a1:}, {q2:, a2:}}

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Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {q2:, a2:}}, {7B: {q1:, a1:}, {q2:, a2:}}

# References |Yao, L., Peng, J., Mao, C., and Luo, Y. (2023).|Exploring large language models for knowledge graph completion.| |---|---| |Zhang, J. (2023).|Graph-toolformer: To empower llms with graph reasoning ability via prompt augmented by chatgpt. arXiv preprint arXiv:2304.11116.| |Zhang, Y., Zhang, Y., Gan, Y., Yao, L., and Wang, C. (2024).|Causal graph discovery with retrieval-augmented generation based large language models. arXiv preprint arXiv:2402.15301.| |Zheng, L., Chiang, W.-L., Sheng, Y., Zhuang, S., Wu, Z., Zhuang, Y., Lin, Z., Li, Z., Li, D., Xing, E., et al.

Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {q2:, a2:}}, {7B: {q1:, a1:}, {q2:, a2:}}

(2024).|Judging llm-as-a-judge with mt-bench and chatbot arena. Advances in Neural Information Processing Systems, 36.|

Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {q2:, a2:}}, {7B: {q1:, a1:}, {q2:, a2:}}

# arXiv:2405.13084v1 [cs.CL] 21 May 2024 Version 1.0 (April 29, 2024) THE 2ND FUTUREDIAL CHALLENGE: DIALOG SYSTEMS WITH RETRIEVAL AUGMENTED GENERATION (FUTUREDIAL-RAG) |Yucheng Cai*|Shi Chen| |---|---| |Tsinghua University|China Mobile Research Institute| |Junlan Feng†| | |China Mobile Research Institute| | |fengjunlan@chinamobile.com| | |Yi Huang| | |China Mobile Research Institute| | |Zhijian Ou*| | |Tsinghua University| | |ozj@tsinghua.edu.cn| | # 1 OVERVIEW Developing intelligent dialog systems has been one of the longest running goals in AI. In recent years, significant progress has been made in building dialog systems with the breakthrough of deep learning methods and the large amount of conversational data being made available for system development (Budzianowski et al., 2018; Ou et al., 2022a; Ouyang et al., 2022; Achiam et al., 2023). There are still full of challenges toward building future dialog systems. The first FutureDial challenge focused on building semi-supervised and reinforced task-oriented dialog systems (FutureDial-SereTOD) (Ou et al., 2022a;b), which was successfully held at EMNLP 2022 SereTOD workshop1. ChatGPT (Ouyang et al., 2022), a newly emerged generative dialog system in the end of 2022, has marked another amazing progress in engaging users in open-domain dialogs. However, problems like hallucination and fabrication (Alkaissi & McFarlane, 2023) still hinder the usage of such systems in real-life applications like customer service systems, which requires pin-point accuracy. Retrieval augmented generation (RAG) (Lewis et al., 2020; Guu et al., 2020) has been introduced to enhance dialog systems with retrieved information from external knowledge bases and has attracted increasing interests. RAG has been shown to be able to help the dialog systems to reply with higher accuracy and factuality, providing more informative and grounded responses (Humeau et al., 2020; Izacard & Grave, 2021; Shuster et al., 2022b; Glass et al., 2022; Izacard et al., 2022a;b; Shuster et al., 2022a; Cai et al., 2023). However, there remain challenges for RAG-based dialog systems such as designing retrievers that can retrieve knowledge from multiple knowledge sources, building RAG-based dialog systems that can effectively utilize available tools and API-calls for retrieval (Schick et al., 2023; Yao et al., 2023), and etc. To further promote the study of how to empower dialog systems with RAG, we release a new dataset, called MobileCS2 (Mobile Customer Service) that aims to benchmark and stimulate related research in this area. The dataset originates from the real-life customer-service logs from China Mobile. Relevant knowledge bases and ground truth retrieved results are annotated so that dialog systems with RAG can be trained over such data. Note that successful fulfilling of customer-service usually needs to call specialized domain knowledge and/or APIs to retrieve relevant information. This dataset is very suited to benchmark dialog systems with RAG.

Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {q2:, a2:}}, {7B: {q1:, a1:}, {q2:, a2:}}

In MobileCS2, there have multiple types of knowledge bases, like user profile, product information and FAQ manual, which bring challenge to the retrieval task in RAG. Moreover, the dataset contains around 3,000 sessions of unlabeled dialogs along with the same amount of sessions of labeled dialogs, which facilitates the study for semi-supervised RAG-based dialog systems (Zhang et al., 2020; Liu et al., 2022a; Cai et al., 2022; Liu et al., 2023; Cai et al., 2023). *Supported by National Science and Technology Major Project (2023ZD0121401) †Corresponding author http://seretod.org/

Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {q2:, a2:}}, {7B: {q1:, a1:}, {q2:, a2:}}

# Version 1.0 (April 29, 2024) Following the success of the 1st FutureDial challenge, the 2nd FutureDial challenge, co-located with SLT 2024, aims to benchmark and stimulate research in building dialog systems with RAG, with the newly released dialog dataset, MobileCS2, as overviewed in Figure 1. We aim to create a forum to discuss key challenges in the field and share findings from real-world applications. # TOPICS OF INTEREST FOR THE CHALLENGE Topics of interest that are relevant to the challenge include, but are not limited to, the following: - Retrieval augmented dialog systems - Information retrieval - Grounded dialog systems with unstructured knowledge sources - Large language model based dialog systems - Holistic AI technologies and applications - Semi-supervised dialog systems - Reinforced dialog systems - Evaluation of dialog systems - Dialog-related datasets and language resources - General topics for dialog systems # SHARED TASK |FutureDial|Semi-supervised and Reinforcement Learning|Retrieval Augmented Generation| |---|---|---| |MobileCS1|100,000+ dialogues with entities; attribute triples and intents|6,000+ dialogues with external knowledge sources and tools| Figure 1: Overview of the FutureDial-RAG Challenge: Dialog Systems with Retrieval Augmented Generation. The 1st FutureDial challenge at EMNLP 2022 (Ou et al., 2022a;b; Liu et al., 2022b) focused on building semi-supervised and reinforced task-oriented dialog systems (FutureDial-SereTOD), and released a large-scale human-human dialog dataset MobileCS1 (Mobile Customer Service).

Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {q2:, a2:}}, {7B: {q1:, a1:}, {q2:, a2:}}