565 citations · 725 across the 92 of their papers we have counts for
10 papers · 2 filters
ToW: Thoughts of Words Improve Reasoning in Large Language Models
Zhikun Xu, Ming Shen, Jacob Dineen +6
We introduce thoughts of words (ToW), a novel training-time data-augmentation method for next-word prediction. ToW views next-word prediction as a core reasoning task and injects f…
Step-by-Step Reasoning to Solve Grid Puzzles: Where do LLMs Falter?
Nemika Tyagi, Mihir Parmar, Mohith Kulkarni +5
Solving grid puzzles involves a significant amount of logical reasoning. Hence, it is a good domain to evaluate the reasoning capability of a model which can then guide us to impro…
Investigating and Addressing Hallucinations of LLMs in Tasks Involving Negation
Neeraj Varshney, Satyam Raj, Venkatesh Mishra +4
Large Language Models (LLMs) have achieved remarkable performance across a wide variety of natural language tasks. However, they have been shown to suffer from a critical limitatio…
Multi-LogiEval: Towards Evaluating Multi-Step Logical Reasoning Ability of Large Language Models
Nisarg Patel, Mohith Kulkarni, Mihir Parmar +4
As Large Language Models (LLMs) continue to exhibit remarkable performance in natural language understanding tasks, there is a crucial need to measure their ability for human-like…
Cutting Through the Noise: Boosting LLM Performance on Math Word Problems
Ujjwala Anantheswaran, Himanshu Gupta, Kevin Scaria +3
Large Language Models (LLMs) excel at various tasks, including solving math word problems (MWPs), but struggle with real-world problems containing irrelevant information. To addres…
Chaos with Keywords: Exposing Large Language Models Sycophantic Hallucination to Misleading Keywords and Evaluating Defense Strategies
Aswin RRV, Nemika Tyagi, Md Nayem Uddin +2
This study explores the sycophantic tendencies of Large Language Models (LLMs), where these models tend to provide answers that match what users want to hear, even if they are not…