25 citations · 28 across the 9 of their papers we have counts for
10 papers
Characterizing Web Search by Conversational LLM Agents: From Search Decisions and Strategies to Results and Responses
Mahsa Amani, Seungeon Lee, Abhisek Dash +9
Conversational LLM agents increasingly rely on Web search, yet the end-to-end lifecycle of agentic search remains poorly understood. We present the first study of Web search across…
On Epistemic Diversity in Large Language Models
Elisabeth Kirsten, Nicole Krämer, Muhammad Bilal Zafar
Large language models (LLMs) are increasingly used not only to retrieve information, but to answer questions, explain, teach, and support inquiry. In such settings, evaluation cann…
To Call or Not to Call: A Framework to Assess and Optimize LLM Tool Calling
Qinyuan Wu, Soumi Das, Mahsa Amani +5
Agentic AI architectures augment LLMs with external tools, unlocking strong capabilities but potentially incurring substantial costs. Moreover, tool use is not always beneficial: r…
The Algorithmic Self-Portrait: Deconstructing Memory in ChatGPT
Abhisek Dash, Soumi Das, Elisabeth Kirsten +6
To enable personalized and context-aware interactions, conversational AI systems have introduced a new mechanism: Memory. Memory creates what we refer to as the Algorithmic Self-po…
Characterizing Web Search in The Age of Generative AI
Elisabeth Kirsten, Jost Grosse Perdekamp, Qinyuan Wu +3
The advent of LLMs has given rise to generative search, a new search paradigm in which LLMs retrieve information from the web related to a query and synthesize it into a single, co…
FinAI-BERT: A Transformer-Based Model for Sentence-Level Detection of AI Disclosures in Financial Reports
Muhammad Bilal Zafar
The proliferation of artificial intelligence (AI) in financial services has prompted growing demand for tools that can systematically detect AI-related disclosures in corporate fil…
Position is Power: System Prompts as a Mechanism of Bias in Large Language Models (LLMs)
Anna Neumann, Elisabeth Kirsten, Muhammad Bilal Zafar +1
System prompts in Large Language Models (LLMs) are predefined directives that guide model behaviour, taking precedence over user inputs in text processing and generation. LLM deplo…
When Should We Orchestrate Multiple Agents?
Umang Bhatt, Sanyam Kapoor, Mihir Upadhyay +6
Strategies for orchestrating the interactions between multiple agents, both human and artificial, can wildly overestimate performance and underestimate the cost of orchestration. W…
Can LLMs Explain Themselves Counterfactually?
Zahra Dehghanighobadi, Asja Fischer, Muhammad Bilal Zafar
Explanations are an important tool for gaining insights into the behavior of ML models, calibrating user trust and ensuring regulatory compliance. Past few years have seen a flurry…
The Impact of Inference Acceleration on Bias of LLMs
Elisabeth Kirsten, Ivan Habernal, Vedant Nanda +1
Last few years have seen unprecedented advances in capabilities of Large Language Models (LLMs). These advancements promise to benefit a vast array of application domains. However,…