2 citations · 9 across the 34 of their papers we have counts for
19 papers · 1 filter
No One Model Catches Every Harm: Benchmarking Content Moderation Across Safety Scenarios
Afshin Orojlooyjadid, Hitesh Patel
Large Language Models (LLMs) are increasingly deployed in real-world applications, yet they remain vulnerable to generating harmful content. From adversarial jailbreaks that bypass…
GSM-SEM: Benchmark and Framework for Generating Semantically Variant Augmentations
Jyotika Singh, Fang Tu, Aziza Mirsaidova +11
Benchmarks like GSM8K are popular measures of mathematical reasoning, but leaderboard gains can overstate true capability due to memorization of fixed test sets. Most robustness va…
Robust Audio-Text Retrieval via Cross-Modal Attention and Hybrid Loss
Meizhu Liu, Matthew Rowe, Amit Agarwal +8
Audio-text retrieval enables semantic alignment between audio content and natural language queries, supporting applications in multimedia search, accessibility, and surveillance. H…
SPENCE: A Syntactic Probe for Detecting Contamination in NL2SQL Benchmarks
Mohammadtaher Safarzadeh, Hitesh Laxmichand Patel, Afshin Orojlooyjadid +2
Large language models (LLMs) have achieved strong performance on natural language to SQL (NL2SQL) benchmarks, yet their reported accuracy may be inflated by contamination from benc…
Think Twice Before You Write -- an Entropy-based Decoding Strategy to Enhance LLM Reasoning
Jiashu He, Meizhu Liu, Olaitan P Olaleye +9
Decoding strategies play a central role in shaping the reasoning ability of large language models (LLMs). Traditional methods such as greedy decoding and beam search often suffer f…
Judging What We Cannot Solve: A Consequence-Based Approach for Oracle-Free Evaluation of Research-Level Math
Guijin Son, Donghun Yang, Hitesh Laxmichand Patel +5
Recent progress in reasoning models suggests that generating plausible attempts for research-level mathematics may be within reach, but verification remains a bottleneck, consuming…
CommonLID: Re-evaluating State-of-the-Art Language Identification Performance on Web Data
Pedro Ortiz Suarez, Laurie Burchell, Catherine Arnett +94
Language identification (LID) is a fundamental step in curating multilingual corpora. However, LID models still perform poorly for many languages, especially on the noisy and heter…
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…
Global PIQA: Evaluating Commonsense Reasoning Across 100+ Languages and Cultures
Tyler A. Chang, Catherine Arnett, Abdelrahman Sadallah +377
To date, there exist almost no culturally-specific evaluation benchmarks for large language models (LLMs) that cover a large number of languages and cultures. In this paper, we pre…
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…
SpeechWeave: Diverse Multilingual Synthetic Text & Audio Data Generation Pipeline for Training Text to Speech Models
Karan Dua, Puneet Mittal, Ranjeet Gupta +1
High-quality Text-to-Speech (TTS) model training requires extensive and diverse text and speech data. It is challenging to procure such data from real sources due to issues of doma…
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…
LLM for Barcodes: Generating Diverse Synthetic Data for Identity Documents
Hitesh Laxmichand Patel, Amit Agarwal, Bhargava Kumar +2
Accurate barcode detection and decoding in Identity documents is crucial for applications like security, healthcare, and education, where reliable data extraction and verification…
Enhancing Document AI Data Generation Through Graph-Based Synthetic Layouts
Amit Agarwal, Hitesh Patel, Priyaranjan Pattnayak +3
The development of robust Document AI models has been constrained by limited access to high-quality, labeled datasets, primarily due to data privacy concerns, scarcity, and the hig…