most citedContext-Enhanced Language Models for Generating Multi-Paper Citations

6 citations · 11 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2024

Enhancing LLMs for Physics Problem-Solving using Reinforcement Learning with Human-AI Feedback

Avinash Anand, Kritarth Prasad, Chhavi Kirtani +6

Large Language Models (LLMs) have demonstrated strong capabilities in text-based tasks but struggle with the complex reasoning required for physics problems, particularly in advanc…

cs.AI2024

Improving Multimodal LLMs Ability In Geometry Problem Solving, Reasoning, And Multistep Scoring

Avinash Anand, Raj Jaiswal, Abhishek Dharmadhikari +6

This paper presents GPSM4K, a comprehensive geometry multimodal dataset tailored to augment the problem-solving capabilities of Large Vision Language Models (LVLMs). GPSM4K encompa…

cs.CL20246 cited

Context-Enhanced Language Models for Generating Multi-Paper Citations

Avinash Anand, Kritarth Prasad, Ujjwal Goel +4

Citation text plays a pivotal role in elucidating the connection between scientific documents, demanding an in-depth comprehension of the cited paper. Constructing citations is oft…

cs.CL20245 cited

Mathify: Evaluating Large Language Models on Mathematical Problem Solving Tasks

Avinash Anand, Mohit Gupta, Kritarth Prasad +5

The rapid progress in the field of natural language processing (NLP) systems and the expansion of large language models (LLMs) have opened up numerous opportunities in the field of…

cs.CL2024

KG-CTG: Citation Generation through Knowledge Graph-guided Large Language Models

Avinash Anand, Mohit Gupta, Kritarth Prasad +4

Citation Text Generation (CTG) is a task in natural language processing (NLP) that aims to produce text that accurately cites or references a cited document within a source documen…