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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 t... | 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:}, {... |
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 diver... | 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:}, {... |
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 Answ... | 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:}, {... |
| |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|5... | 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:}, {... |
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 dive... | 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:}, {... |
| |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... | 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:}, {... |
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... | 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:}, {... |
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 h... | 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:}, {... |
# 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., 202... | 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:}, {... |
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 tar... | 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:}, {... |
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. ... | 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:}, {... |
# 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-Pon... | 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:}, {... |
(2023). Knowledge-augmented language model prompting for zero-shot knowledge graph question answering. arXiv preprint arXiv:2306.04136.
Ban, T., Chen, L., Wang, X., and Chen, H. (2023). From query tools to causal architects: Harnessing large language models for advanced causal discovery from data.
Baumel, T., Eyal, M... | 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:}, {... |
(2023). Retrieval-augmented generation for large language models: A survey. arXiv preprint arXiv:2312.10997.
| 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:}, {... |
Goodwin, T. R., Savery, M. E., and Demner-Fushman, D. (2020). | 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:}, {... |
Flight of the pegasus? comparing transformers on few-shot and zero-shot multi-document abstractive summarization. In Proceedings of COLING. International Conference on Computational Linguistics, volume 2020, page 5640. NIH Public Access.
He, X., Tian, Y., Sun, Y., Chawla, N. V., Laurent, T., LeCun, Y., Bresson, X., an... | 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:}, {... |
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|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., 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:}, {... |
(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:}, {... |
# 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| |
|fengjun... | 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:}, {... |
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 th... | 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:}, {... |
# 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 fo... | 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:}, {... |
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