# Enhancing Reasoning Capacity of SLM using Cognitive Enhancement

arXiv: https://arxiv.org/abs/2404.01135 · HTML: https://mlsanity.com/paper/2404.01135/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 20 of its 37 references that are in the map.
- First posted on arXiv 1 Apr 2024.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)

## Builds on (most cited first, 20 of 20)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [Latent Retrieval for Weakly Supervised Open Domain Question Answering](https://mlsanity.com/paper/1906.00300/) (2019)
- [Understanding the planning of LLM agents: A survey](https://mlsanity.com/paper/2402.02716/) (2024)
- [DoReMi: Optimizing Data Mixtures Speeds Up Language Model Pretraining](https://mlsanity.com/paper/2305.10429/) (2023)
- [Teaching Small Language Models to Reason](https://mlsanity.com/paper/2212.08410/) (2022)
- [Log-based Anomaly Detection Without Log Parsing](https://mlsanity.com/paper/2108.01955/) (2021)
- [Metacognitive Prompting Improves Understanding in Large Language Models](https://mlsanity.com/paper/2308.05342/) (2023)
- [Experience Report: Deep Learning-based System Log Analysis for Anomaly Detection](https://mlsanity.com/paper/2107.05908/) (2021)
- [Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research](https://mlsanity.com/paper/2208.14937/) (2022)
- [RAGLog: Log Anomaly Detection using Retrieval Augmented Generation](https://mlsanity.com/paper/2311.05261/) (2023)
- [ChatGPT for Digital Forensic Investigation: The Good, The Bad, and The Unknown](https://mlsanity.com/paper/2307.10195/) (2023)
- [An Assessment of ChatGPT on Log Data](https://mlsanity.com/paper/2309.07938/) (2023)
- [Resource-rational Task Decomposition to Minimize Planning Costs](https://mlsanity.com/paper/2007.13862/) (2020)

## Built on by (most cited first, 1 of 1)

- [REFLEX: Reference-Free Evaluation of Log Summarization via Large Language Model Judgment](https://mlsanity.com/paper/2511.07458/) (2025)

## Abstract (arXiv)

Large Language Models (LLMs) have been applied to automate cyber security activities and processes including cyber investigation and digital forensics. However, the use of such models for cyber investigation and digital forensics should address accountability and security considerations. Accountability ensures models have the means to provide explainable reasonings and outcomes. This information can be extracted through explicit prompt requests. For security considerations, it is crucial to address privacy and confidentiality of the involved data during data processing as well. One approach to deal with this consideration is to have the data processed locally using a local instance of the model. Due to limitations of locally available resources, namely memory and GPU capacities, a Smaller Large Language Model (SLM) will typically be used. These SLMs have significantly fewer parameters compared to the LLMs. However, such size reductions have notable performance reduction, especially when tasked to provide reasoning explanations. In this paper, we aim to mitigate performance reduction through the integration of cognitive strategies that humans use for problem-solving. We term this as cognitive enhancement through prompts. Our experiments showed significant improvement gains of the SLMs' performances when such enhancements were applied. We believe that our exploration study paves the way for further investigation into the use of cognitive enhancement to optimize SLM for cyber security applications.