2 citations · 8 across the 27 of their papers we have counts for
8 papers · 2 filters
Aligning LLMs for Multilingual Consistency in Enterprise Applications
Amit Agarwal, Hansa Meghwani, Hitesh Laxmichand Patel +3
Large language models (LLMs) remain unreliable for global enterprise applications due to substantial performance gaps between high-resource and mid/low-resource languages, driven b…
Pushing on Multilingual Reasoning Models with Language-Mixed Chain-of-Thought
Guijin Son, Donghun Yang, Hitesh Laxmichand Patel +9
Recent frontier models employ long chain-of-thought reasoning to explore solution spaces in context and achieve stonger performance. While many works study distillation to build sm…
AccessEval: Benchmarking Disability Bias in Large Language Models
Srikant Panda, Amit Agarwal, Hitesh Laxmichand Patel
Large Language Models (LLMs) are increasingly deployed across diverse domains but often exhibit disparities in how they handle real-life queries. To systematically investigate thes…
Who's Asking? Investigating Bias Through the Lens of Disability Framed Queries in LLMs
Vishnu Hari, Kalpana Panda, Srikant Panda +2
Large Language Models (LLMs) routinely infer users demographic traits from phrasing alone, which can result in biased responses, even when no explicit demographic information is pr…
DAIQ: Auditing Demographic Attribute Inference from Question in LLMs
Srikant Panda, Hitesh Laxmichand Patel, Shahad Al-Khalifa +3
Recent evaluations of Large language models (LLMs) audit social bias primarily through prompts that explicitly reference demographic attributes, overlooking whether models infer se…
SweEval: Do LLMs Really Swear? A Safety Benchmark for Testing Limits for Enterprise Use
Hitesh Laxmichand Patel, Amit Agarwal, Arion Das +6
Enterprise customers are increasingly adopting Large Language Models (LLMs) for critical communication tasks, such as drafting emails, crafting sales pitches, and composing casual…
Tokenization Matters: Improving Zero-Shot NER for Indic Languages
Priyaranjan Pattnayak, Hitesh Laxmichand Patel, Amit Agarwal
Tokenization is a critical component of Natural Language Processing (NLP), especially for low resource languages, where subword segmentation influences vocabulary structure and dow…
Clinical QA 2.0: Multi-Task Learning for Answer Extraction and Categorization
Priyaranjan Pattnayak, Hitesh Laxmichand Patel, Amit Agarwal +3
Clinical Question Answering (CQA) plays a crucial role in medical decision-making, enabling physicians to extract relevant information from Electronic Medical Records (EMRs). While…