3 citations · 7 across the 12 of their papers we have counts for
12 papers
ToolAlignBench: Investigating Alignment Conflicts in Tool-Calling Enabled LLMs
Aryan Keluskar, Amrita Bhattacharjee, Huan Liu
Safety alignment in LLMs aims to align models with human values, but which values take precedence when they conflict? We investigate this question in the context of tool-calling LL…
When Does Personality Composition Matter for Multi-Agent LLM Teams?
Aryan Keluskar, Amrita Bhattacharjee, Huan Liu
Personality prompting shapes how large language models communicate, yet whether these behavioral shifts affect objective task outcomes remains under-explored. Prior work shows that…
AgentOS: From Application Silos to a Natural Language-Driven Data Ecosystem
Rui Liu, Tao Zhe, Dongjie Wang +5
The rapid emergence of open-source, locally hosted intelligent agents marks a critical inflection point in human-computer interaction. Systems such as OpenClaw demonstrate that Lar…
Generalization in Online Reinforcement Learning for Mobile Agents
Li Gu, Zihuan Jiang, Zhixiang Chi +5
Graphical user interface (GUI)-based mobile agents automate digital tasks on mobile devices by interpreting natural-language instructions and interacting with the screen. While rec…
Do LLMs Understand Ambiguity in Text? A Case Study in Open-world Question Answering
Aryan Keluskar, Amrita Bhattacharjee, Huan Liu
Ambiguity in natural language poses significant challenges to Large Language Models (LLMs) used for open-domain question answering. LLMs often struggle with the inherent uncertaint…
Defending Against Social Engineering Attacks in the Age of LLMs
Lin Ai, Tharindu Kumarage, Amrita Bhattacharjee +12
The proliferation of Large Language Models (LLMs) poses challenges in detecting and mitigating digital deception, as these models can emulate human conversational patterns and faci…
Extracting Heuristics from Large Language Models for Reward Shaping in Reinforcement Learning
Siddhant Bhambri, Amrita Bhattacharjee, Durgesh Kalwar +3
Reinforcement Learning (RL) suffers from sample inefficiency in sparse reward domains, and the problem is further pronounced in case of stochastic transitions. To improve the sampl…
Zero-shot LLM-guided Counterfactual Generation: A Case Study on NLP Model Evaluation
Amrita Bhattacharjee, Raha Moraffah, Joshua Garland +1
With the development and proliferation of large, complex, black-box models for solving many natural language processing (NLP) tasks, there is also an increasing necessity of method…
EAGLE: A Domain Generalization Framework for AI-generated Text Detection
Amrita Bhattacharjee, Raha Moraffah, Joshua Garland +1
With the advancement in capabilities of Large Language Models (LLMs), one major step in the responsible and safe use of such LLMs is to be able to detect text generated by these mo…
Towards Interpretable Hate Speech Detection using Large Language Model-extracted Rationales
Ayushi Nirmal, Amrita Bhattacharjee, Paras Sheth +1
Although social media platforms are a prominent arena for users to engage in interpersonal discussions and express opinions, the facade and anonymity offered by social media may al…
Adversarial Text Purification: A Large Language Model Approach for Defense
Raha Moraffah, Shubh Khandelwal, Amrita Bhattacharjee +1
Adversarial purification is a defense mechanism for safeguarding classifiers against adversarial attacks without knowing the type of attacks or training of the classifier. These te…
ResumeFlow: An LLM-facilitated Pipeline for Personalized Resume Generation and Refinement
Saurabh Bhausaheb Zinjad, Amrita Bhattacharjee, Amey Bhilegaonkar +1
Crafting the ideal, job-specific resume is a challenging task for many job applicants, especially for early-career applicants. While it is highly recommended that applicants tailor…