2 citations · 2 across the 3 of their papers we have counts for
3 papers
Efficient Reasoning Distillation: Small Video-Language Models via Synthetic CoT and Difficulty-Aware Fine-Tuning
Mantek Singh, Jeshwanth Challagundla, Siddharth Raina +1
We present an efficient method to distill reasoning capabilities into compact video-language models (VLMs) for video question answering (VideoQA). Our approach fine-tunes a 2B-para…
LLMs as Master Forgers: Generating Synthetic Time Series Data for Manufacturing
Mantek Singh, Jeshwanth Challagundla, Prateek Karnal +3
This paper presents a novel framework leveraging Large Language Models (LLMs) to generate synthetic time series data for manufacturing processes. Motivated by the scarcity of label…
SI-Agent: An Agentic Framework for Feedback-Driven Generation and Tuning of Human-Readable System Instructions for Large Language Models
Jeshwanth Challagundla, Mantek Singh, Siddharth Raina +3
System Instructions (SIs), or system prompts, are pivotal for guiding Large Language Models (LLMs) but manual crafting is resource-intensive and often suboptimal. Existing automate…