11 citations · 19 across the 26 of their papers we have counts for
31 papers
FIGS: Evaluating Multi-Turn Sycophancy Without Penalizing Empathy
Sidharth Pulipaka, Ruta Binkyte, Ivaxi Sheth +1
Large language models frequently fail to balance staying truthful with being supportive. They often exhibit sycophancy in responses to users, agreeing with false claims, offering u…
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…
Fabrication After Tool Failure: Tool-Augmented Agents Assert Values Their Tools Did Not Return
Arham Sethi, Arsen Kenzhebayev, Saanvi Paturi +3
Tool-augmented language models are evaluated on whether they reach the right answer, not on whether they report honestly when a tool fails to supply one. We isolate this post-failu…
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…
Mitigating Over-Personalization in LLMs via Structured Memory
Hakeem Hannoon, Andrew Zhao, Mihir Narayan +2
Conversational assistants increasingly rely on persistent long-term memory to personalize responses across sessions. However, when stored user information is reintroduced into the…
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.…
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…
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…
PersistBench: When Should Long-Term Memories Be Forgotten by LLMs?
Sidharth Pulipaka, Oliver Chen, Manas Sharma +3
Conversational assistants are increasingly integrating long-term memory with large language models (LLMs). This persistence of memories, e.g., the user is vegetarian, can enhance p…
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…
MedG-KRP: Medical Graph Knowledge Representation Probing
Gabriel R. Rosenbaum, Lavender Yao Jiang, Ivaxi Sheth +11
Large language models (LLMs) have recently emerged as powerful tools, finding many medical applications. LLMs' ability to coalesce vast amounts of information from many sources to…
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…
Context-Aware Reasoning On Parametric Knowledge for Inferring Causal Variables
Ivaxi Sheth, Sahar Abdelnabi, Mario Fritz
Scientific discovery catalyzes human intellectual advances, driven by the cycle of hypothesis generation, experimental design, evaluation, and assumption refinement. Central to thi…
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…
Auxiliary Losses for Learning Generalizable Concept-based Models
Ivaxi Sheth, Samira Ebrahimi Kahou
The increasing use of neural networks in various applications has lead to increasing apprehensions, underscoring the necessity to understand their operations beyond mere final pred…
Survey on AI Ethics: A Socio-technical Perspective
Dave Mbiazi, Meghana Bhange, Maryam Babaei +3
The past decade has observed a significant advancement in AI with deep learning-based models being deployed in diverse scenarios, including safety-critical applications. As these A…
Transparent Anomaly Detection via Concept-based Explanations
Laya Rafiee Sevyeri, Ivaxi Sheth, Farhood Farahnak +2
Advancements in deep learning techniques have given a boost to the performance of anomaly detection. However, real-world and safety-critical applications demand a level of transpar…
WiCV@CVPR2023: The Eleventh Women In Computer Vision Workshop at the Annual CVPR Conference
Doris Antensteiner, Marah Halawa, Asra Aslam +6
In this paper, we present the details of Women in Computer Vision Workshop - WiCV 2023, organized alongside the hybrid CVPR 2023 in Vancouver, Canada. WiCV aims to amplify the voic…