# Exploring the Limits of Large Language Models: A Systematic Evaluation of Masked Text Processing Ability through MskQA and MskCal

arXiv: https://arxiv.org/abs/2411.05665 · HTML: https://mlsanity.com/paper/2411.05665/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 19 references that are in the map.
- First posted on arXiv 8 Nov 2024.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Emergent Abilities of Large Language Models](https://mlsanity.com/paper/2206.07682/) (2022)
- [Program Induction by Rationale Generation : Learning to Solve and Explain Algebraic Word Problems](https://mlsanity.com/paper/1705.04146/) (2017)
- [MASS: Masked Sequence to Sequence Pre-training for Language Generation](https://mlsanity.com/paper/1905.02450/) (2019)
- [RealTime QA: What's the Answer Right Now?](https://mlsanity.com/paper/2207.13332/) (2022)
- [Masked Language Modeling and the Distributional Hypothesis: Order Word Matters Pre-training for Little](https://mlsanity.com/paper/2104.06644/) (2021)
- [DREAM: A Challenge Dataset and Models for Dialogue-Based Reading Comprehension](https://mlsanity.com/paper/1902.00164/) (2019)
- [UnNatural Language Inference](https://mlsanity.com/paper/2101.00010/) (2020)
- [Word Order Does Matter (And Shuffled Language Models Know It)](https://mlsanity.com/paper/2203.10995/) (2022)
- [Unnatural Error Correction: GPT-4 Can Almost Perfectly Handle Unnatural Scrambled Text](https://mlsanity.com/paper/2311.18805/) (2023)
- [Word Order's Impacts: Insights from Reordering and Generation Analysis](https://mlsanity.com/paper/2403.11473/) (2024)

## Abstract (arXiv)

This paper sheds light on the limitations of Large Language Models (LLMs) by rigorously evaluating their ability to process masked text. We introduce two novel tasks: MskQA, measuring reasoning on masked question-answering datasets like RealtimeQA, and MskCal, assessing numerical reasoning on masked arithmetic problems.Testing GPT-4o and 4o-mini reveals that while LLMs exhibit some resilience to masked text, their performance is highly contingent on masking rates and semantic cues. Specifically, "solid masking," where semantic clues are entirely absent, leads to a significant performance drop compared to "partial lifting," where some semantic information is retained, indicating LLMs' reliance on surface-level patterns. Interestingly, GPT-4o consistently outperforms 4o-mini, particularly in MskCal, demonstrating a greater ability to handle numerical reasoning with masked text. This underscores the crucial role of semantic cues in the reasoning process of LLMs. Our study illuminates the interplay between background knowledge and reasoning ability in masked text processing, paving the way for a deeper understanding of LLM capabilities and limitations, and highlighting the need for more robust evaluation methods to accurately assess their true comprehension abilities.