allenai/openscilm_queries huggingface dataset:
Query: We will first survey several papers that are in the intersection of AI and healthcare, specifically NLP tasks which use a lot of the large language models that have come into existence these days. 
We will then attempt to categorize the different perspectives that people have highlighted in terms of the security and privacy issues with large language models such as personally identifiable data in the pre-training data. 
In terms of categorization, we are looking to categorize these across two dimensions.
Operational perspective: The perspective of applications in healthcare, such as applications to patient care, education, practitioner burden, etc.
Technical perspective: The perspective of developing these LLMs which has multiple stages such as pre-training, data filtering, architecture choice, alignment, and evaluations. 
We will then, in the discussion section, talk about the different areas that we have identified where there are gaps and how we propose further research to help fill those gaps in terms of privacy and security in LLMs. In medical NLP tasks. 

Subject: computer science
Intent: Literature Understanding
Query: As a solution to improve maintenance efficiency and reduce the risk of failure, predictive maintenance (PdM) is applied, a data-driven maintenance approach that aims to extend the life cycle of a system by predicting potential equipment failures in a timely manner. This allows for reduced equipment downtime (Incardona et al., 2024). One of the latest promising approaches in PdM is Deep Reinforcement Learning (DRL), which combines the advantages of Deep Learning (DL) and Reinforcement Learning (RL) to build more adaptive and data-driven maintenance models (Zhao et al., 2023; Ong et al., 2020).
Subject: engineering
Intent: Other
Query: What are the techniques used to study the following:

Science of LMs: scaling laws, fundamental limitations, emergent capabilities, demystification, interpretability, complexity, training dynamics, grokking, learning theory for LMs
Compute efficient LMs: distillation, compression, quantization, sample efficient methods, memory efficient methods
Learning algorithms for LMs: learning, unlearning, meta learning, model mixing methods, continual learning
Inference algorithms for LMs: decoding algorithms, reasoning algorithms, search algorithms, planning algorithms

Subject: computer science
Intent: Literature Understanding
Query: i have a section of my paper about crypto triangular arbitrage i want u to help me add some citations to it 'In financial markets, inefficiencies such as these often exist only for brief periods before being corrected by the market itself. As a result, speed and accuracy are critical factors when identifying and acting on arbitrage opportunities [2]. The importance of triangular arbitrage lies in its ability to exploit these fleeting moments of price discrepancy in currency markets, allowing traders to gain profits with minimal risk. For investors, the ability to identify these opportunities is crucial, as modern financial markets are highly competitive, and mispricing windows are typically narrow [3].' only keep my text just add citations
Subject: finance
Intent: Paper Finding
Query: Upon binding to the GLP-1 receptor (GLP-1R), GLP-1RAs undergo arrestin-mediated internalization. The extent of this internalization is inversely correlated with signal transduction, resulting in a reduced blood glucose-lowering effect with higher internalization rates. Conversely, lower internalization rates lead to sustained activation of cell surface receptors, facilitating a more pronounced reduction in blood glucose levels.
Subject: medicine
Intent: Other
Query: Client Selection and Scheduling
Client selection significantly impacts FL convergence and
robustness. FedCSA [17] assigns clients a composite anomaly-
quality score and filters low-scoring clients over training
rounds. While effective, it lacks adaptive scheduling under
wireless constraints and does not optimize communication
resources. RL-based approaches  have been applied
to schedule clients in wireless FL, focusing on energy and
latency trade-offs, but without security awareness
Subject: computer science
Intent: Other
Query: Check for prior work: We propose the Dynamic Persona Adaptation Network (DPAN), a novel architecture for continuously
updating an agent's persona representation based on its interactions. DPAN consists of three main
components: 3. Interaction Generator: Produces contextually appropriate responses based on the
updated persona embedding and current conversation state. The system is trained end-to-end using a
combination of supervised learning on annotated multi-agent conversations and reinforcement learning
with rewards for maintaining consistent long-term behavior while appropriately adapting to social cues.
• Context Encoder: Processes the ongoing conversation and social dynamics, extracting relevant
features from the agent's own utterances, other agents' responses, and overall conversation flow.
• Persona Update Module: Generates incremental updates to the persona embedding based on the
encoded context. This module learns to identify relevant social cues and adjust the persona
accordingly.
• Interaction Generator: Produces contextually appropriate responses based on the updated persona
embedding and current conversation state.
Subject: computer science
Intent: Literature Understanding
Query: I am seeking information on the existence of a communication system that is capable of measuring distance between nodes by means of round-trip time (RTT) and also achieves highly accurate timing synchronisation. Additionally, I am interested in learning whether there are any academic papers that have implemented such a communication system. It is my preference to focus on signal processing-level implementations and to include as many papers from the Institute of Electrical and Electronics Engineers (IEEE) as possible.
Subject: engineering
Intent: Paper Finding
Query: find papers for the following literature review and questions
write paragraphs around the following questions

Topic: Innovative problem-solving in collaborative scenario using AI
This literature review is about investigating works related to collaborative
group problem solving especially around innovative design tasks using AI
tools. Here are some potential questions to explore:
1.Why and how would people solve problems better when they work as a
group?
2.What are the group interactions like when the group scale is different?
For large-scale collective problem-solving and design innovation, like
collective innovation contests, what strategies are used to improve people’s
overall performance?
3.In what way can AI-enabled tools change the group dynamics and help
with finding creative ideas?
4.Are AI tools have effects on process and implicit outcomes of the
teamwork besides the final visible artifacts?
Subject: computer science
Intent: Paper Finding
Query: Rewrite below in a more academic form:
The authors of \cite{li2024synergizing} provided a comprehensive analysis of the Federated Learning framework's wireless communication component, pointing out possible avenues for future WFL research per the 6G goal. They underlined the close relationship between WFL's efficacy and the underlying wireless communication network's capabilities. Additionally, their research highlighted that to enable the smooth integration of WFL within the framework of 6G, all devices use a multiple-access approach to upload their uniquely trained outcomes during the model transmission phase of each communication round. The data exchange forms a critical part of WFL; thus, ensuring efficiency and reliability is a must \cite{bouzinis2021wireless}.
Subject: social sciences
Intent: Other