221 citations · 616 across the 46 of their papers we have counts for
48 papers
Evidence-Informed LLM Beliefs for Continual Scientific Discovery
Dhruv Agarwal, Reece Adamson, Andrew McCallum +3
Open-ended scientific discovery with large language models (LLMs) increasingly operates as a long-horizon loop of hypothesis search and verification, where a reward signal guides w…
Process-Oriented Evaluation of AI-Assisted Scientific Writing
Patrick Queiroz Da Silva, Sanchaita Hazra, Doeun Lee +2
Bad writing hinders the publication of science. The role of artificial intelligence (AI) in generating and editing scientific texts remains unsettled. Abstracts serve as the critic…
ArtifactLinker: Linking Scientific Artifacts for Automatic State-of-the-Art Discovery
Haofei Yu, Jiaxuan You, Peter Clark +2
Scientific artifacts such as models and datasets are foundations for research. With the rapid growth of platforms like HuggingFace, researchers now have access to a large number of…
Understanding Usage and Engagement in AI-Powered Scientific Research Tools: The Asta Interaction Dataset
Dany Haddad, Dan Bareket, Joseph Chee Chang +19
AI-powered scientific research tools are rapidly being integrated into research workflows, yet the field lacks a clear lens into how researchers use these systems in real-world set…
Accepted with Minor Revisions: Value of AI-Assisted Scientific Writing
Sanchaita Hazra, Doeun Lee, Bodhisattwa Prasad Majumder +1
Large Language Models have seen expanding application across domains, yet their effectiveness as assistive tools for scientific writing - an endeavor requiring precision, multimoda…
AstaBench: Rigorous Benchmarking of AI Agents with a Scientific Research Suite
Jonathan Bragg, Mike D'Arcy, Nishant Balepur +36
AI agents hold the potential to revolutionize scientific productivity by automating literature reviews, replicating experiments, analyzing data, and even proposing new directions o…
ScholarEval: Research Idea Evaluation Grounded in Literature
Hanane Nour Moussa, Patrick Queiroz Da Silva, Daniel Adu-Ampratwum +7
As AI tools become increasingly common for research ideation, robust evaluation is critical to ensure the validity and usefulness of generated ideas. We introduce ScholarEval, a re…
Sotopia-RL: Reward Design for Social Intelligence
Haofei Yu, Zhengyang Qi, Yining Zhao +6
Social intelligence has become a critical capability for large language models (LLMs), enabling them to engage effectively in real-world social tasks such as collaboration and nego…
AutoDiscovery: Open-ended Scientific Discovery via Bayesian Surprise
Dhruv Agarwal, Bodhisattwa Prasad Majumder, Reece Adamson +8
The promise of autonomous scientific discovery (ASD) hinges not only on answering questions, but also on knowing which questions to ask. Most recent works in ASD explore the use of…
A Survey on Personalized and Pluralistic Preference Alignment in Large Language Models
Zhouhang Xie, Junda Wu, Yiran Shen +9
Personalized preference alignment for large language models (LLMs), the process of tailoring LLMs to individual users' preferences, is an emerging research direction spanning the a…
AI Safety Should Prioritize the Future of Work
Sanchaita Hazra, Bodhisattwa Prasad Majumder, Tuhin Chakrabarty
Current efforts in AI safety prioritize filtering harmful content, preventing manipulation of human behavior, and eliminating existential risks in cybersecurity or biosecurity. Whi…
CodeScientist: End-to-End Semi-Automated Scientific Discovery with Code-based Experimentation
Peter Jansen, Oyvind Tafjord, Marissa Radensky +6
Despite the surge of interest in autonomous scientific discovery (ASD) of software artifacts (e.g., improved ML algorithms), current ASD systems face two key limitations: (1) they…
Latent Factor Models Meets Instructions: Goal-conditioned Latent Factor Discovery without Task Supervision
Zhouhang Xie, Tushar Khot, Bhavana Dalvi Mishra +4
Instruction-following LLMs have recently allowed systems to discover hidden concepts from a collection of unstructured documents based on a natural language description of the purp…
Towards Implicit Bias Detection and Mitigation in Multi-Agent LLM Interactions
Angana Borah, Rada Mihalcea
As Large Language Models (LLMs) continue to evolve, they are increasingly being employed in numerous studies to simulate societies and execute diverse social tasks. However, LLMs a…
DiscoveryBench: Towards Data-Driven Discovery with Large Language Models
Bodhisattwa Prasad Majumder, Harshit Surana, Dhruv Agarwal +7
Can the rapid advances in code generation, function calling, and data analysis using large language models (LLMs) help automate the search and verification of hypotheses purely fro…
DISCOVERYWORLD: A Virtual Environment for Developing and Evaluating Automated Scientific Discovery Agents
Peter Jansen, Marc-Alexandre Côté, Tushar Khot +5
Automated scientific discovery promises to accelerate progress across scientific domains. However, developing and evaluating an AI agent's capacity for end-to-end scientific reason…
Few-shot Dialogue Strategy Learning for Motivational Interviewing via Inductive Reasoning
Zhouhang Xie, Bodhisattwa Prasad Majumder, Mengjie Zhao +4
We consider the task of building a dialogue system that can motivate users to adopt positive lifestyle changes: Motivational Interviewing. Addressing such a task requires a system…
Tell, Don't Show!: Language Guidance Eases Transfer Across Domains in Images and Videos
Tarun Kalluri, Bodhisattwa Prasad Majumder, Manmohan Chandraker
We introduce LaGTran, a novel framework that utilizes text supervision to guide robust transfer of discriminative knowledge from labeled source to unlabeled target data with domain…
Data-driven Discovery with Large Generative Models
Bodhisattwa Prasad Majumder, Harshit Surana, Dhruv Agarwal +3
With the accumulation of data at an unprecedented rate, its potential to fuel scientific discovery is growing exponentially. This position paper urges the Machine Learning (ML) com…
Skill Set Optimization: Reinforcing Language Model Behavior via Transferable Skills
Kolby Nottingham, Bodhisattwa Prasad Majumder, Bhavana Dalvi Mishra +3
Large language models (LLMs) have recently been used for sequential decision making in interactive environments. However, leveraging environment reward signals for continual LLM ac…
Tailoring with Targeted Precision: Edit-Based Agents for Open-Domain Procedure Customization
Yash Kumar Lal, Li Zhang, Faeze Brahman +3
How-to procedures, such as how to plant a garden, are now used by millions of users, but sometimes need customizing to meet a user's specific needs, e.g., planting a garden without…
To Tell The Truth: Language of Deception and Language Models
Sanchaita Hazra, Bodhisattwa Prasad Majumder
Text-based misinformation permeates online discourses, yet evidence of people's ability to discern truth from such deceptive textual content is scarce. We analyze a novel TV game s…
CLIN: A Continually Learning Language Agent for Rapid Task Adaptation and Generalization
Bodhisattwa Prasad Majumder, Bhavana Dalvi Mishra, Peter Jansen +5
Language agents have shown some ability to interact with an external environment, e.g., a virtual world such as ScienceWorld, to perform complex tasks, e.g., growing a plant, witho…
Put Your Money Where Your Mouth Is: Evaluating Strategic Planning and Execution of LLM Agents in an Auction Arena
Jiangjie Chen, Siyu Yuan, Rong Ye +2
Recent advancements in Large Language Models (LLMs) showcase advanced reasoning, yet NLP evaluations often depend on static benchmarks. Evaluating this necessitates environments th…