1 citations · 3 across the 10 of their papers we have counts for
13 papers
Style over Substance? Linguistic Bias in LLM-based Peer Review Copy title
Sai Suresh Macharla Vasu, Sumedh Santosh Joshi, Philipp Christmann +6
The number of paper submissions to major AI conferences has increased dramatically in recent years, substantially increasing the peer-review workload of researchers. At the same ti…
Share-Borne AI Virus: Memory-Hopping Attacks Across LLM Agents
Sidharth Pulipaka, Ansh Sharma, Stanislau Hlebik +4
Large language models are increasingly deployed as stateful assistants that retain information across interactions and use tools to read, modify, and create persistent artifacts. A…
Trustworthy AI Suffers from Invariance Conflicts and Causality is The Solution
Ruta Binkyte, Ivaxi Sheth, Zhijing Jin +3
As artificial intelligence (AI), including machine learning (ML) models and foundation models (FMs), are increasingly deployed in high-stakes domains, ensuring their trustworthines…
Hidden in Memory: Sleeper Memory Poisoning in LLM Agents
Sidharth Pulipaka, Stanislau Hlebik, Leonidas Raghav +4
Large language models are increasingly augmented with persistent memory, allowing assistants to store user-specific information across sessions for personalization and continuity.…
Inspectable AI for Science: A Research Object Approach to Generative AI Governance
Ruta Binkyte, Sharif Abuaddba, Chamikara Mahawaga +3
This paper introduces AI as a Research Object (AI-RO), a paradigm for governing the use of generative AI in scientific research. Instead of debating whether AI is an author or mere…
IV Co-Scientist: Multi-Agent LLM Framework for Causal Instrumental Variable Discovery
Ivaxi Sheth, Zhijing Jin, Bryan Wilder +2
In the presence of confounding between an endogenous variable and the outcome, instrumental variables (IVs) are used to isolate the causal effect of the endogenous variable. Identi…
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…
Justice in Judgment: Unveiling (Hidden) Bias in LLM-assisted Peer Reviews
Sai Suresh Macharla Vasu, Ivaxi Sheth, Hui-Po Wang +2
The adoption of large language models (LLMs) is transforming the peer review process, from assisting reviewers in writing detailed evaluations to generating entire reviews automati…
ProtocolLLM: RTL Benchmark for SystemVerilog Generation of Communication Protocols
Arnav Sheth, Ivaxi Sheth, Mario Fritz
Recent advances in large language models (LLMs) have demonstrated strong performance in generating code for general-purpose programming languages. However, their potential for hard…
Causality Is Key to Understand and Balance Multiple Goals in Trustworthy ML and Foundation Models
Ruta Binkyte, Ivaxi Sheth, Zhijing Jin +3
Ensuring trustworthiness in machine learning (ML) systems is crucial as they become increasingly embedded in high-stakes domains. This paper advocates for integrating causal method…
Safety Must Precede the Deployment of Open-Ended AI
Ivaxi Sheth, Jan Wehner, Sahar Abdelnabi +2
AI advancements have been significantly driven by a combination of foundation models and curiosity-driven learning aimed at increasing capability and adaptability. Within this land…
LLM4GRN: Discovering Causal Gene Regulatory Networks with LLMs -- Evaluation through Synthetic Data Generation
Tejumade Afonja, Ivaxi Sheth, Ruta Binkyte +4
Gene regulatory networks (GRNs) represent the causal relationships between transcription factors (TFs) and target genes in single-cell RNA sequencing (scRNA-seq) data. Understandin…
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…