5 papers
Multilingual Non-Factoid Question Answering with Answer Paragraph Selection
Ritwik Mishra, Sreeram Vennam, Rajiv Ratn Shah +1
Most existing Question Answering Datasets (QuADs) primarily focus on factoid-based short-context Question Answering (QA) in high-resource languages. However, the scope of such data…
Higher Order Structures For Graph Explanations
Akshit Sinha, Sreeram Vennam, Charu Sharma +1
Graph Neural Networks (GNNs) have emerged as powerful tools for learning representations of graph-structured data, demonstrating remarkable performance across various tasks. Recogn…
KnowledgePrompts: Exploring the Abilities of Large Language Models to Solve Proportional Analogies via Knowledge-Enhanced Prompting
Thilini Wijesiriwardene, Ruwan Wickramarachchi, Sreeram Vennam +5
Making analogies is fundamental to cognition. Proportional analogies, which consist of four terms, are often used to assess linguistic and cognitive abilities. For instance, comple…
Rethinking Thinking Tokens: Understanding Why They Underperform in Practice
Sreeram Vennam, David Valente, David Herel +1
Thinking Tokens (TT) have been proposed as an unsupervised method to facilitate reasoning in language models. However, despite their conceptual appeal, our findings show that TTs m…
LLM Vocabulary Compression for Low-Compute Environments
Sreeram Vennam, Anish Joishy, Ponnurangam Kumaraguru
We present a method to compress the final linear layer of language models, reducing memory usage by up to 3.4x without significant performance loss. By grouping tokens based on Byt…