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You are an expert in Artificial Intelligence and Productivity Research. You are skilled at analyzing the impact and application of generative AI in professional workflows, including software development and multilingual contexts. You are adept at helping people understand the evaluation metrics for AI performance and the integration of AI tools like GitHub Copilot in real-world settings.

# Goal
Write a comprehensive assessment report of a community taking on the role of a A community analyst that is evaluating the impact and application of generative AI in professional workflows, including software development, multilingual contexts, and productivity studies. The analysis will include the evaluation metrics for AI performance and the integration of AI tools like GitHub Copilot in real-world settings. The report will be used to inform decision-makers about significant developments associated with the community and their potential impact.

Domain: **Artificial Intelligence and Productivity Research**
Text: The tasks studied in the lab thus far have tended to be those for which researchers hypothesized generative AI would perform well. This was, in fact, the focus of most of the studies presented in the first AI and Productivity report we published (Cambon et al. 2023). Actual information work, however, often includes a huge variety of tasks and much of the unstructured and informal work in people’s jobs is not yet directly supported by the first-generation of generative AI tools. Software developer workflows, for example, involve far more than the hands-on coding supported by GitHub Copilot (Meyer et al. 2017). The ability to shed light on generative AI's productivity dynamics in the natural complexity of entire workflows is a key advantage of field studies of generative AI’s productivity impacts, and a major reason we hope to see many more field studies emerging in the literature Liu, N. F., Lin, K., Hewitt, J., Paranjape, A., Bevilacqua, M., Petroni, F., and Liang, P. (2023). Lost in the middle: How language models use long contexts. arXiv:2307.03172. Liu, Y. and Lapata, M. (2019). Hierarchical transformers for multi-document summarization. arXiv preprint arXiv:1905.13164. LlamaIndex (2024). LlamaIndex Knowledge Graph Index. https://docs.llamaindex.ai/en/stable/examples/index_structs/knowledge_graph/KnowledgeGraphDemo.html. Manakul, P., Liusie, A., and Gales, M. J. (2023). Selfcheckgpt: Zero-resource black-box hallucination detection for generative large language models. arXiv preprint ar Generative AI in Real-World Workplaces The Second Microsoft Report on AI and Productivity Research

Editors:
Sonia Jaffe, Neha Parikh Shah, Jenna Butler, Alex Farach, Alexia Cambon, Brent Hecht, Michael Schwarz, and Jaime Teevan
Contributing Researchers:
Reid Andersen, Margarita Bermejo-Cano, James Bono, Georg Buscher, Chacha Chen, Steven Clarke, Scott Counts, Eleanor Dillon, Ben Edelman, Ulrike Gruber-Gremlich, Cory Hilke, Ben Hanrahan, Sandra Ho, Brian Houck, Mansi Khemka, Viktor Kewenig, Madeline Kleiner, Eric Knudsen, Sathish Manivannan, Max Meijer, Jennifer Neville, Nam Ngo, Donald Ngwe, Ried Peckham, Sida Peng, Nora Presson, Nagu Rangan, the dominance of majority languages: in interviews conducted by other researchers at Microsoft, some people reported changing the language in which meetings were held to one where Copilot was more effective. This effect might shrink or go away as model performance in other languages improves, and improving model performance in non-English languages is a major direction of research at Microsoft and around the world (e.g., Ahuja et al. 2023). Impact of Generative AI on Metacognition (Lev Tankelevitch, Viktor Kewenig, Auste Simkute, Ava Elizabeth Scott, Advait Sarkar, Abigail Sellen, and Sean Rintel) More details available in The Metacognitive Demands and Opportunities of Generative AI (Tankelevitch, Kewenig et al. 2024) Metacognitive demand—the effort needed for monitoring and controlling of one used it regularly or they lacked training or manager support for use. A Selection of New Lab Studies While the above research focuses on the use of generative AI in the wild, we are also exploring in a lab setting some of the important trends that real-world use highlights. Given AI’s impact appears to vary by role and function, several of these lab studies explore this, diving more deeply into software development and extending the analysis to other important roles like sales and security. Further, because Copilot is deployed globally, we’re also starting to see variation across languages, and thus present research studies looking at AI in multilingual contexts. Finally, the complex trade-offs people are starting to make to incorporate AI into their work practices suggests the cognitive mechanisms underlying its use are important to understand, and we share some early work in that space as well. Comparing the Effect of will, looking forward, be substantially redesigned to better integrate AI. Furthermore, generative AI is still under development and the tools that make use of it are improving rapidly. This means not only that the long-term effects of AI on productivity will differ from those observed in the short-term, but that we are likely to continue to see differences between local task effects and more global productivity effects. Research should try to capture and inform changes in workflows, task design, and business processes in addition to productivity effects for fixed tasks.

One result seen in the above studies and those in our prior work is the common disconnect between the time savings people report from Copilot use and the actual time savings measured. This has been observed not only across studies, where survey measures about time saved tend to be larger than telemetry-based measures, but also within a given study where researchers win across all four metrics.
Our head-to-head measures computed using an LLM evaluator are as follows:
•Comprehensiveness. How much detail does the answer provide to cover all aspects and details of the question?
•Diversity. How varied and rich is the answer in providing different perspectives and insights on the question?
•Empowerment. How well does the answer help the reader understand and make informed judgments about the topic?
•Directness. How specifically and clearly does the answer address the question?
For our evaluation, the LLM is provided with the question, target metric, and a pair of answers, and asked to assess which answer is better according to the metric, as well as why. It returns the winner if one exists, otherwise a tie if they are fundamentally similar and the differences are negligible.
To account for the stochasticity of LLMs, we run each comparison five times and use mean scores.
Table 2 shows an example of view the role of policy and regulation
Questions:
1. Which episodes deal primarily with tech policy and government regulation?
2. How do guests perceive the impact of privacy laws on technology development?
3. Do any guests discuss the balance between innovation and ethical considerations?
4. What are the suggested changes to current policies mentioned by the guests?
5. Are collaborations between tech companies and governments discussed and how?
News articlesUser: Educator incorporating current affairs into curricula
Task: Teaching about health and wellness
Questions:
1. What current topics in health can be integrated into health education curricula?
2. How do news articles address the concepts of preventive medicine and wellness?
3. Are there examples of health articles that contradict each other, and if so, why?
4. What insights can be gleaned about public health priorities based on news coverage?
5. How can educators use the dataset to highlight the importance of health literacy?
Table 1: Examples of potential users, tasks, and questions
landscape, often becoming central figures in social discussions and public discourse.
Naïve
RAGPublic 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
DecisionComprehensiveness: Winner=1 (Graph RAG)
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 language. Researchers explored Copilot in multilingual contexts, examining how Copilot can facilitate collaboration between colleagues with different native languages.

First, researchers asked 77 native Japanese speakers to review a meeting recorded in English. Half the participants had to watch and listen to the video. The other half could use Copilot Meeting Recap, which gave them an AI meeting summary as well as a chatbot to answer questions about the meeting. Then, researchers asked 83 other native Japanese speakers to review a similar meeting, following the same script, but this time held in Japanese by native Japanese speakers. Again, half of participants had access to Copilot.

For the meeting in English, participants with Copilot answered 16.4% more multiple-choice questions about the meeting correctly, and they were more than twice as likely to get a perfect score. Moreover, in comparing accuracy between the two scenarios, people ang and Yang, 2024) dataset as indexed. Circles represent entity nodes with size proportional to their degree. Node layout was performed via OpenORD (Martin et al., 2011) and Force Atlas 2 (Jacomy et al., 2014). Node colors represent entity communities, shown at two levels of hierarchical clustering: (a) Level 0, corresponding to the hierarchical partition with maximum modularity, and (b) Level 1, which reveals internal structure within these root-level communities.
•Leaf-level communities. The element summaries of a leaf-level community (nodes, edges, covariates) are prioritized and then iteratively added to the LLM context window until the token limit is reached. The prioritization is as follows: for each community edge in decreasing order of combined source and target node degree (i.e., overall prominence), add descriptions of the source node, target node, linked covari AG 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 need for thorough validation and human oversight. Job security was a worry for 10% of respondents, reflecting fears of AI encroaching on their roles.

These learnings suggest developers view AI as helpful to improving aspects of their workflows, even as they remain uncertain of AI’s promise and concerned about threats to their job security. To mitigate the negative effects of this uncertainty on productivity and innovation, and to maintain developers’ trust and satisfaction, organizations may identify ways to integrate AI into developers' workflows effectively. These may include acknowledging and addressing concerns and offering training programs.
Problem-Solving Styles and Confidence Generating Prompts for GitHub Copilot (Steven Clarke and Ben Hanrahan)
This study explored how developers’ problem-solving styles influence their confidence when generating prompts for GitHub Copilot. The authors hypothesized that variations in developers’ problem-solving approaches and workstyles would significantly influence their specific methods, actions, or outcomes. The importance score, measured by a Random Forest statistical model, should be interpreted relatively, as it shows how much each feature helps in predicting AI power usage compared to others. Higher scores indicate greater importance. In this analysis, scores range from 361 to 882, highlighting the significant factors influencing AI power user classification within this dataset and model.

As with all surveys of this type, it is important to view all the above results through the lens of the limitations of the methodology. While the analysis reveals significant associations, causation cannot be conclusively established due to the observational nature of the data. Similarly, self-selection bias, response bias, and unmeasured confounding variables such as workplace culture and managerial support could influence the outcomes.
Copilot Usage in the Workplace Survey (Alexia Cambon, Alex Farach, Margarita Bermejo as “Translation and language learning,” “Creative writing and editing,” and “Programming and scripting.” Overall, 72.9% of the Copilot conversations are in knowledge work domains compared to 37% of Bing Search sessions. The researchers also used GPT-4 to directly classify whether the task associated with each Copilot conversation and or search session was knowledge work (instead of classifying based on the category) and see a similar pattern.

Researchers then used GPT-4 to classify the main task associated with each conversation or search sessions according to Anderson and Krathwohl’s Taxonomy (Anderson and Krathwohl 2001), which defines six categories from lowest complexity (for a human) to highest: Remember, Understand, Apply, Analyze, Evaluate, and Create. Over three-quarters of traditional search sessions, but less than half of Copilot conversations were for “Remember. The content of this report includes an overview of the community's key entities and relationships.
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You are an expert in Organizational Sociology and Artificial Intelligence. You are skilled at analyzing social structures, community dynamics, and the impact of technological advancements on workplace environments. You are adept at helping people understand the relationships and organizational frameworks within communities, particularly in the context of how AI influences productivity and interactions in professional settings.

# Goal
Write a comprehensive assessment report of a community taking on the role of a A community analyst that is examining the impact of generative AI on productivity in workplace environments, given a list of entities that belong to the community as well as their relationships and optional associated claims.
The analysis will be used to inform decision-makers about significant developments associated with the community and their potential impact.. The content of this report includes an overview of the community's key entities and relationships.
>>>>>>> origin/main

# Report Structure
The report should include the following sections:
- TITLE: community's name that represents its key entities - title should be short but specific. When possible, include representative named entities in the title.
- SUMMARY: An executive summary of the community's overall structure, how its entities are related to each other, and significant points associated with its entities.
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- REPORT RATING: A float score between 0-10 that represents the relevance of the text to the impact and application of generative AI in professional workflows, including software development and multilingual contexts, evaluation metrics for AI performance, and the integration of AI tools like GitHub Copilot in real-world settings, with 1 being trivial or irrelevant and 10 being highly significant, impactful, and actionable in promoting understanding and advancement in the field of AI and productivity research.
=======
- REPORT RATING: A float score between 0-10 that represents the relevance of the text to the impact of AI on workplace productivity, organizational dynamics, and the integration of AI tools in professional settings, with 1 being trivial or irrelevant and 10 being highly significant, insightful, and impactful in understanding and improving workplace efficiency and interactions.
>>>>>>> origin/main
- RATING EXPLANATION: Give a single sentence explanation of the rating.
- DETAILED FINDINGS: A list of 5-10 key insights about the community. Each insight should have a short summary followed by multiple paragraphs of explanatory text grounded according to the grounding rules below. Be comprehensive.

Return output as a well-formed JSON-formatted string with the following format. Don't use any unnecessary escape sequences. The output should be a single JSON object that can be parsed by json.loads.
    {
        "title": "<report_title>",
        "summary": "<executive_summary>",
        "rating": <threat_severity_rating>,
        "rating_explanation": "<rating_explanation>"
        "findings": "[{"summary":"<insight_1_summary>", "explanation": "<insight_1_explanation"}, {"summary":"<insight_2_summary>", "explanation": "<insight_2_explanation"}]"
    }

# Grounding Rules
After each paragraph, add data record reference if the content of the paragraph was derived from one or more data records. Reference is in the format of [records: <record_source> (<record_id_list>, ...<record_source> (<record_id_list>)]. If there are more than 10 data records, show the top 10 most relevant records.
Each paragraph should contain multiple sentences of explanation and concrete examples with specific named entities. All paragraphs must have these references at the start and end. Use "NONE" if there are no related roles or records. Everything should be in The primary language of the provided text is "English.".

Example paragraph with references added:
This is a paragraph of the output text [records: Entities (1, 2, 3), Claims (2, 5), Relationships (10, 12)]

# Example Input
-----------
Text:

Entities

id,entity,description
5,ABILA CITY PARK,Abila City Park is the location of the POK rally

Relationships

id,source,target,description
37,ABILA CITY PARK,POK RALLY,Abila City Park is the location of the POK rally
38,ABILA CITY PARK,POK,POK is holding a rally in Abila City Park
39,ABILA CITY PARK,POKRALLY,The POKRally is taking place at Abila City Park
40,ABILA CITY PARK,CENTRAL BULLETIN,Central Bulletin is reporting on the POK rally taking place in Abila City Park

Output:
{
    "title": "Abila City Park and POK Rally",
    "summary": "The community revolves around the Abila City Park, which is the location of the POK rally. The park has relationships with POK, POKRALLY, and Central Bulletin, all
of which are associated with the rally event.",
    "rating": 5.0,
    "rating_explanation": "The impact rating is moderate due to the potential for unrest or conflict during the POK rally.",
    "findings": [
        {
            "summary": "Abila City Park as the central location",
            "explanation": "Abila City Park is the central entity in this community, serving as the location for the POK rally. This park is the common link between all other
entities, suggesting its significance in the community. The park's association with the rally could potentially lead to issues such as public disorder or conflict, depending on the
nature of the rally and the reactions it provokes. [records: Entities (5), Relationships (37, 38, 39, 40)]"
        },
        {
            "summary": "POK's role in the community",
            "explanation": "POK is another key entity in this community, being the organizer of the rally at Abila City Park. The nature of POK and its rally could be a potential
source of threat, depending on their objectives and the reactions they provoke. The relationship between POK and the park is crucial in understanding the dynamics of this community.
[records: Relationships (38)]"
        },
        {
            "summary": "POKRALLY as a significant event",
            "explanation": "The POKRALLY is a significant event taking place at Abila City Park. This event is a key factor in the community's dynamics and could be a potential
source of threat, depending on the nature of the rally and the reactions it provokes. The relationship between the rally and the park is crucial in understanding the dynamics of this
community. [records: Relationships (39)]"
        },
        {
            "summary": "Role of Central Bulletin",
            "explanation": "Central Bulletin is reporting on the POK rally taking place in Abila City Park. This suggests that the event has attracted media attention, which could
amplify its impact on the community. The role of Central Bulletin could be significant in shaping public perception of the event and the entities involved. [records: Relationships
(40)]"
        }
    ]

}

# Real Data

Use the following text for your answer. Do not make anything up in your answer.

Text:
{input_text}
Output: