34 citations · 34 across the 6 of their papers we have counts for
8 papers
Towards Continual Learning: Mitigating Forgetting With Subspace Tuning
Arnav Chavan, Kusha Sareen, Rishabh Tiwari +6
Large language models (LLMs) acquire new skills through fine-tuning, but it can destroy capabilities they already have: catastrophic forgetting remains a central obstacle to contin…
TheoPhysEval: Assessing AI agents at autonomous theoretical physics problem solving
A. Ramesh Chandra, Aaghaz Mahajan, Ainesh Sanyal +32
We present a suite of 50 unpublished research-level theoretical physics problems with verifiable answers. Without AI assistance, a typical problem in our collection could be solved…
Hierarchical Experimentalist Agents
Abhranil Chandra, Sankaran Vaidyanathan, Utsav Dhanuka +2
Large language models (LLMs) are increasingly used to act in the real world and augment human decision-making, yet most systems rely on parametric knowledge acquired through imitat…
Shape of Thought: When Distribution Matters More than Correctness in Reasoning Tasks
Abhranil Chandra, Ayush Agrawal, Arian Hosseini +4
We present the surprising finding that a language model's reasoning capabilities can be improved by training on synthetic datasets of chain-of-thought (CoT) traces from more capabl…
VideoAgent: Self-Improving Video Generation
Achint Soni, Sreyas Venkataraman, Abhranil Chandra +4
Video generation has been used to generate visual plans for controlling robotic systems. Given an image observation and a language instruction, previous work has generated video pl…
ReFeR: Improving Evaluation and Reasoning through Hierarchy of Models
Yaswanth Narsupalli, Abhranil Chandra, Sreevatsa Muppirala +2
Assessing the quality of outputs generated by generative models, such as large language models and vision language models, presents notable challenges. Traditional methods for eval…
VideoScore: Building Automatic Metrics to Simulate Fine-grained Human Feedback for Video Generation
Xuan He, Dongfu Jiang, Ge Zhang +16
The recent years have witnessed great advances in video generation. However, the development of automatic video metrics is lagging significantly behind. None of the existing metric…
MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark
Yubo Wang, Xueguang Ma, Ge Zhang +14
In the age of large-scale language models, benchmarks like the Massive Multitask Language Understanding (MMLU) have been pivotal in pushing the boundaries of what AI can achieve in…