7 papers
A Hybrid Supervised-LLM Pipeline for Actionable Suggestion Mining in Unstructured Customer Reviews
Aakash Trivedi, Aniket Upadhyay, Pratik Narang +2
Extracting actionable suggestions from customer reviews is essential for operational decision-making, yet these directives are often embedded within mixed-intent, unstructured text…
Actionable Advice from Reviews via Mixture of LoRA Experts: A Two-LLM Pipeline for Issue Extraction and Business Recommendations
Kartikey Singh Bhandari, Manav Ganesh, Yashwant Viswanathan +3
Customer reviews contain detailed, domain specific signals about service failures and user expectations, but converting this unstructured feedback into actionable business decision…
Understanding Virality: A Rubric based Vision-Language Model Framework for Short-Form Edutainment Evaluation
Arnav Gupta, Gurekas Singh Sahney, Hardik Rathi +4
Evaluating short-form video content requires moving beyond surface-level quality metrics toward human-aligned, multimodal reasoning. While existing frameworks like VideoScore-2 ass…
Investigating Spatial Attention Bias in Vision-Language Models
Aryan Chaudhary, Sanchit Goyal, Pratik Narang +1
Vision-Language Models have demonstrated remarkable capabilities in understanding visual content, yet systematic biases in their spatial processing remain largely unexplored. This…
Large Language Models as Pokémon Battle Agents: Strategic Play and Content Generation
Daksh Jain, Aarya Jain, Ashutosh Desai +4
Strategic decision-making in Pokémon battles presents a unique testbed for evaluating large language models. Pokémon battles demand reasoning about type matchups, statistical trade…
From Facts to Conclusions : Integrating Deductive Reasoning in Retrieval-Augmented LLMs
Shubham Mishra, Samyek Jain, Gorang Mehrishi +4
Retrieval-Augmented Generation (RAG) grounds large language models (LLMs) in external evidence, but fails when retrieved sources conflict or contain outdated or subjective informat…