40 citations · 116 across the 23 of their papers we have counts for
5 papers · 1 filter
Noisy Exemplars Make Large Language Models More Robust: A Domain-Agnostic Behavioral Analysis
Hongyi Zheng, Abulhair Saparov
Recent advances in prompt engineering enable large language models (LLMs) to solve multi-hop logical reasoning problems with impressive accuracy. However, there is little existing…
Retrieval-Augmented Chain-of-Thought in Semi-structured Domains
Vaibhav Mavi, Abulhair Saparov, Chen Zhao
Applying existing question answering (QA) systems to specialized domains like law and finance presents challenges that necessitate domain expertise. Although large language models…
Personas as a Way to Model Truthfulness in Language Models
Nitish Joshi, Javier Rando, Abulhair Saparov +2
Large language models (LLMs) are trained on vast amounts of text from the internet, which contains both factual and misleading information about the world. While unintuitive from a…
World Models for Math Story Problems
Andreas Opedal, Niklas Stoehr, Abulhair Saparov +1
Solving math story problems is a complex task for students and NLP models alike, requiring them to understand the world as described in the story and reason over it to compute an a…
Testing the General Deductive Reasoning Capacity of Large Language Models Using OOD Examples
Abulhair Saparov, Richard Yuanzhe Pang, Vishakh Padmakumar +4
Given the intractably large size of the space of proofs, any model that is capable of general deductive reasoning must generalize to proofs of greater complexity. Recent studies ha…