3 papers
cs.AI2026
More Than a Quick Glance: Overcoming the Greedy Bias in KV-Cache Compression
Aryan Sood, Tanvi Sharma, Vansh Agrawal
While Large Language Models (LLMs) can theoretically support extensive context windows, their actual deployment is constrained by the linear growth of Key-Value (KV) cache memory.…
cs.CL2024
Give me a hint: Can LLMs take a hint to solve math problems?
Vansh Agrawal, Pratham Singla, Amitoj Singh Miglani +2
While state-of-the-art LLMs have shown poor logical and basic mathematical reasoning, recent works try to improve their problem-solving abilities using prompting techniques. We pro…
cs.CV2024
Beyond Captioning: Task-Specific Prompting for Improved VLM Performance in Mathematical Reasoning
Ayush Singh, Mansi Gupta, Shivank Garg +2
Vision-Language Models (VLMs) have transformed tasks requiring visual and reasoning abilities, such as image retrieval and Visual Question Answering (VQA). Despite their success, V…