3 citations · 5 across the 9 of their papers we have counts for
12 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…
MPSelectTune: Prompt-type Selection for Fine-tuning improves Concept Unlearning in LLMs
Shubhadip Nag, Srinjoy Das, Agniva Saha +5
LLMs can be conveniently adapted to a diverse set of tasks, e.g, prediction, question-answering tasks, etc, using appropriate prompts with few-shot examples. Biased or harmful conc…
Fine-tuning vs. In-context Learning in Large Language Models: A Formal Language Learning Perspective
Bishwamittra Ghosh, Soumi Das, Till Speicher +5
Large language models (LLMs) operate in two fundamental learning modes - fine-tuning (FT) and in-context learning (ICL) - raising key questions about which mode yields greater lang…
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…
In Agents We Trust, but Who Do Agents Trust? Latent Source Preferences Steer LLM Generations
Mohammad Aflah Khan, Mahsa Amani, Soumi Das +5
Agents based on Large Language Models (LLMs) are increasingly being deployed as interfaces to information on online platforms. These agents filter, prioritize, and synthesize infor…
LoRA on the Go: Instance-level Dynamic LoRA Selection and Merging
Seungeon Lee, Soumi Das, Manish Gupta +1
Low-Rank Adaptation (LoRA) has emerged as a parameter-efficient approach for fine-tuning large language models. However, conventional LoRA adapters are typically trained for a sing…
Understanding the Interplay between Memorization and Learning in Large Language Models
Bishwamittra Ghosh, Soumi Das, Qinyuan Wu +4
We investigate foundational questions about the interplay between memorization and learning in large language models (LLMs). When an LLM generates a string, can we disentangle the…
Rote Learning Considered Useful: Generalizing over Memorized Data in LLMs
Qinyuan Wu, Soumi Das, Mahsa Amani +4
Rote learning is a memorization technique based on repetition. Many researchers argue that rote learning hinders generalization because it encourages verbatim memorization rather t…
Revisiting Privacy, Utility, and Efficiency Trade-offs when Fine-Tuning Large Language Models
Soumi Das, Camila Kolling, Mohammad Aflah Khan +5
We study the inherent trade-offs in minimizing privacy risks and maximizing utility, while maintaining high computational efficiency, when fine-tuning large language models (LLMs).…
Understanding Memorisation in LLMs: Dynamics, Influencing Factors, and Implications
Till Speicher, Mohammad Aflah Khan, Qinyuan Wu +5
Understanding whether and to what extent large language models (LLMs) have memorised training data has important implications for the reliability of their output and the privacy of…
Towards Reliable Latent Knowledge Estimation in LLMs: Zero-Prompt Many-Shot Based Factual Knowledge Extraction
Qinyuan Wu, Mohammad Aflah Khan, Soumi Das +7
In this paper, we focus on the challenging task of reliably estimating factual knowledge that is embedded inside large language models (LLMs). To avoid reliability concerns with pr…