作者位次 #3/4ICLR 2026Poster· Deep Learning / Everything Else
ReFeR: Improving Evaluation and Reasoning through Hierarchy of Models
Abhranil Chandra · Sreevatsa Muppirala · Manish Gupta · Pawan Goyal
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Abhranil Chandra · Sreevatsa Muppirala · Manish Gupta · Pawan Goyal
Seungeon Lee · Soumi Das · Manish Gupta · Krishna P. Gummadi
Low-Rank Adaptation (LoRA) has emerged as a parameter-efficient approach for fine-tuning large language models. However, conventional LoRA adapters are typically trained for a single task, limiting their applicability in real-world settings, where inputs may span multiple, diverse task domains. A...
Sarmistha Das · Vaibhav Vishal · Syed Ibrahim Ahmad · Sriparna Saha · Manish Gupta
Financial decision-making in multilingual settings demands accurate numerical reasoning grounded in diverse modalities, yet existing benchmarks largely overlook this high-stakes, real-world challenge, especially for Indic languages. We introduce FinVQA, a benchmark for evaluating financial numeri...
Priyanshu Karmakar · Soumyabrata Chaudhuri · Shubhojit Mallick · Manish Gupta · Abhik Jana · Shreya Ghosh
Recent work, such as TripCraft and TravelPlanner, has shown the promise of Large Language Models (LLMs) for personalized, constraint-aware travel itinerary generation. However, real-world travel often involves disruptions such as transit cancellations, weather-related closures, or overbooked attr...
Rahul Mehta · Kavin R V · Indrajit Pal · Tushar Abhishek · Pawan Goyal · Manish Gupta
Query auto-completion (QAC) has been widely studied in the context of web search, yet remains underexplored for in-document search, which we term DocQAC. DocQAC aims to enhance search productivity within long documents by helping users craft faster, more precise queries, even for complex or hard-...