Analysis of Over-Reasoning in Large Language Models

Core Concepts

Large language models tend to generate redundant calculations and reasoning, impacting their performance.

Abstract

The paper discusses the issue of over-reasoning and redundant calculations in large language models (LLMs).
LLMs tend to generate unnecessary calculations even for questions that can be answered without any complex reasoning.
The study introduces GSM8K-Zero, a dataset highlighting LLMs' tendency for redundancy.
Experiments show that LLMs exhibit high redundancy rates, affecting accuracy and performance.
Proxy reward models prefer lengthy answers, leading to redundant outputs from LLMs.
Suggestions are made for future research to address redundancy issues in LLMs.

Stats

"Therefore, the deep-sea monster has consumed approximately 2.03 x 1090 people over three hundred years."
"We find that LLMs tend to generate redundant calculations that complicate the responses and sometimes lead to the wrong answer."

Quotes

"We construct and release a dataset, GSM8K-Zero, which reveals the LLMs’ tendency to generate redundant reasonings."
"LLMs may not differentiate questions requiring step-by-step reasoning from simpler ones."

Key Insights Distilled From

Over-Reasoning and Redundant Calculation of Large Language Models

by Cheng-Han Ch... at arxiv.org 03-21-2024

https://arxiv.org/pdf/2401.11467.pdf

Deeper Inquiries

How can training techniques be developed to teach LLMs when to think step-by-step?

Training techniques can be developed to teach Large Language Models (LLMs) when to engage in step-by-step reasoning by incorporating specific prompts and feedback mechanisms during the training process. One approach is to provide explicit instructions during fine-tuning that guide the model on when it should apply complex reasoning steps. By including prompts that indicate whether a question requires detailed calculations or can be answered directly, LLMs can learn to differentiate between scenarios where step-by-step reasoning is necessary and where it is not.
Additionally, reinforcement learning with human feedback (RLHF) can be utilized to train LLMs more effectively. By providing targeted feedback on the appropriateness of the model's responses, especially regarding redundancy in calculations, LLMs can learn from their mistakes and adjust their behavior accordingly. Incorporating rewards for concise and accurate answers while penalizing unnecessary complexity can help reinforce desirable behaviors in LLMs.
Moreover, introducing diverse datasets with varying levels of complexity and encouraging self-assessment mechanisms within the models themselves could aid in developing a better understanding of when intricate reasoning processes are required. This way, LLMs can adapt their decision-making based on contextual cues present in the input data.

Do non-RLHF-trained LLMs also exhibit redundancy in their outputs?

Yes, non-RLHF-trained Large Language Models (LLMs) also demonstrate redundancy in their outputs under certain conditions. While RLHF-trained models have been specifically designed to improve performance through iterative feedback loops that focus on refining responses over time, non-RLHF-trained models may still generate redundant calculations due to inherent biases or limitations within their architecture.
Nonetheless, without direct human intervention or specialized training procedures like RLHF provides, these models may struggle more with discerning when extensive reasoning steps are necessary versus situations where simpler answers suffice. The lack of continuous feedback loops could result in less adaptive behavior regarding output generation and potentially lead to increased instances of redundant calculations compared to RLHF-trained counterparts.
To mitigate this issue for non-RLHF-trained LLMs, researchers could explore alternative methods such as pre-training strategies that emphasize efficiency and accuracy over verbosity. Additionally, incorporating diverse evaluation metrics focused on reducing redundancy could encourage these models towards generating more concise and relevant responses across various tasks.

How can the preference for lengthy answers by proxy RMs impact the overall performance of LLMs?

The preference for lengthy answers by proxy Reward Models (RMs), as observed through experiments using ChatGPT and GPT-4 as proxies for RM preferences between long and short responses, has significant implications for overall Large Language Model (LLM) performance.
When proxy RMs consistently favor verbose outputs containing redundant calculations even if they are incorrect,
this bias towards lengthier explanations may influence how trained language models prioritize response generation.
This inclination towards verbosity might lead an LMM astray from producing succinct yet accurate solutions,
potentially affecting user experience negatively.
Furthermore,
the tendency towards longer but incorrect answers exhibited by some proxy RMs highlights a critical challenge
as it indicates that certain LM architectures might prioritize quantity over quality
when generating responses.
In practical applications,
this preference could result in misleading or convoluted information being presented
to users instead of clear-cut solutions.
Addressing this issue necessitates reevaluating reward structures used during LM training
and ensuring that incentives align with producing precise,
relevant information rather than merely focusing on response length.
By recalibrating RM preferences toward prioritizing correctness
and relevance over verbosity alone,
future iterations of LM systems stand poised
to deliver more effective outcomes across various domains while minimizing redundancies
in generated content