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20212026
most citedLLM4GRN: Discovering Causal Gene Regulatory Networks with LLMs -- Evaluation through Synthetic Data Generation

1 citations · 4 across the 20 of their papers we have counts for

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cs.CL2026

When Tools Get in the Way: The Effect of Unnecessary Tool Availability on LLM Answering

Saanvi Paturi, Arsen Kenzhebayev, Arham Sethi +3

Large language models (LLMs) are increasingly deployed with external tools that extend what they can do beyond their own knowledge. Tools help on tasks that need external informati…

cs.CL2026

Cross-Lingual LLM-Judge Transfer via Evaluation Decomposition

Ivaxi Sheth, Zeno Jonke, Amin Mantrach +1

As large language models are increasingly deployed across diverse real-world applications, extending automated evaluation beyond English has become a critical challenge. Existing e…

cs.CL2026

Funny or Persuasive, but Not Both: Evaluating Fine-Grained Multi-Concept Control in LLMs

Arya Labroo, Ivaxi Sheth, Vyas Raina +2

Large Language Models (LLMs) offer strong generative capabilities, but many applications require explicit and \textit{fine-grained} control over specific textual concepts, such as…

cs.CL2024

CausalGraph2LLM: Evaluating LLMs for Causal Queries

Ivaxi Sheth, Bahare Fatemi, Mario Fritz

Causality is essential in scientific research, enabling researchers to interpret true relationships between variables. These causal relationships are often represented by causal gr…

cs.CL2024

LLM Task Interference: An Initial Study on the Impact of Task-Switch in Conversational History

Akash Gupta, Ivaxi Sheth, Vyas Raina +2

With the recent emergence of powerful instruction-tuned large language models (LLMs), various helpful conversational Artificial Intelligence (AI) systems have been deployed across…