3 citations · 6 across the 11 of their papers we have counts for
14 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…
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…
Fractional Rotation, Full Potential? Investigating Performance and Convergence of Partial RoPE
Mohammad Aflah Khan, Krishna P. Gummadi, Manish Gupta +1
Rotary Positional Embedding (RoPE) is widely used in transformers to encode relative positional information, yet the effect of applying RoPE to only a subset of hidden dimensions r…
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…
Hubble: a Model Suite to Advance the Study of LLM Memorization
Johnny Tian-Zheng Wei, Ameya Godbole, Mohammad Aflah Khan +7
We present Hubble, a suite of fully open-source large language models (LLMs) for the scientific study of LLM memorization. Hubble models come in standard and perturbed variants: st…
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…
TokenSmith: Streamlining Data Editing, Search, and Inspection for Large-Scale Language Model Training and Interpretability
Mohammad Aflah Khan, Ameya Godbole, Johnny Tian-Zheng Wei +5
Understanding the relationship between training data and model behavior during pretraining is crucial, but existing workflows make this process cumbersome, fragmented, and often in…
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…
Lawma: The Power of Specialization for Legal Annotation
Ricardo Dominguez-Olmedo, Vedant Nanda, Rediet Abebe +6
Annotation and classification of legal text are central components of empirical legal research. Traditionally, these tasks are often delegated to trained research assistants. Motiv…
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…