3 citations · 5 across the 7 of their papers we have counts for
7 papers
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…
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…
SaGE: Evaluating Moral Consistency in Large Language Models
Vamshi Krishna Bonagiri, Sreeram Vennam, Priyanshul Govil +2
Despite recent advancements showcasing the impressive capabilities of Large Language Models (LLMs) in conversational systems, we show that even state-of-the-art LLMs are morally in…
Measuring Moral Inconsistencies in Large Language Models
Vamshi Krishna Bonagiri, Sreeram Vennam, Manas Gaur +1
A Large Language Model (LLM) is considered consistent if semantically equivalent prompts produce semantically equivalent responses. Despite recent advancements showcasing the impre…