Title: Buffer of Thoughts: Thought-Augmented Reasoning with Large Language Models
Venue: NeurIPS 2024
Accepted: yes
Keywords: Large Language Models
Abstract: We introduce Buffer of Thoughts (BoT), a novel and versatile thought-augmented reasoning approach for enhancing accuracy, efficiency and robustness of large language models (LLMs). Specifically, we propose meta-buffer to store a series of informative high-level thoughts, namely thought-template, distilled from the problem-solving processes across various tasks. Then for each problem, we retrieve a relevant thought-template and adaptively instantiate it with specific reasoning structures to conduct efficient reasoning. To guarantee the scalability and stability, we further propose buffer-manager to dynamically update the meta-buffer, thus enhancing the capacity of meta-buffer as more tasks are solved. We conduct extensive experiments on 10 challenging reasoning-intensive tasks, and achieve significant performance improvements over previous SOTA methods: 11\% on Game of 24, 20\% on Geometric Shapes and 51\% on Checkmate-in-One. Further analysis demonstrate the superior generalization ability and model robustness of our BoT, while requiring only 12\% of the cost of multi-query prompting methods (e.g., tree/graph of thoughts) on average. Code is available at: https://github.com/YangLing0818/buffer-of-thought-llm

Buffer of Thoughts: Thought-Augmented Reasoning with Large Language Models

Abstract

We introduce Buffer of Thoughts (BoT), a novel and versatile thought-augmented reasoning approach for enhancing accuracy, efficiency and robustness of large language models (LLMs). Specifically, we propose meta-buffer to store a series of informative high-level thoughts, namely thought-template , distilled from the problem-solving processes across various tasks. Then for each problem, we retrieve a relevant thought-template and adaptively instantiate it with specific reasoning structures to conduct efficient reasoning. To guarantee the scalability and stability, we further propose buffer-manager to dynamically update the meta-buffer, thus enhancing the capacity of meta-buffer as more tasks are solved. We conduct extensive experiments on 10 challenging reasoning-intensive tasks, and achieve significant performance improvements over previous SOTA methods: 11% on Game of 24, 20% on Geometric Shapes and 51% on Checkmate-in-One. Further analysis demonstrate the superior generalization ability and model robustness of our BoT, while requiring only 12% of the cost of multi-query prompting methods (e.g., tree/graph of thoughts) on average. Notably, we find that our Llama3-8B + BoT has the potential to surpass Llama3-70B model. Our project is available at https://github.com/YangLing0818/buffer-of-thought-llm

1 Introduction

A series of Large Language Models (LLMs) [1-5] like GPT-4 [3], PaLM [2] and LLaMA [6, 7] have showcased the impressive performance in various reasoning tasks. In addition to scaling up the model size to improve the reasoning performance, there are more effective prompting methods that further enhance the functionality and performance of LLMs. We divide these methods into two categories: (i) single-query reasoning: these methods [8-10] usually focus on prompt engineering and their reasoning process can be finished within a single query, such as CoT [8] that appends the input query with 'Let's think step by step' to produce rationales for increasing reasoning accuracy, and Few-shot Prompting [11, 12, 9, 13] which provides task-relevant exemplars to assist the answer generation; (ii) multi-query reasoning: these methods [14, 15] focus on leveraging multiple LLM queries to elicit different plausible reasoning paths, thus decomposing a complex problem into a series of simpler sub-problems, such as Least-to-Most [16], ToT [14] and GoT [17].

However, both kinds of methods face some limitations: (1) single-query reasoning usually requires prior assumption or relevant exemplars of reasoning process, which makes it impractical to manually design them task by task, thus lacking universality and generalization; (2) Due to the recursive

LLM

LLM

LLM

Input query

Reasoning

Single-Query

Output

Manual Prompt for Specific Task

(e.g., CoT, Few-shot  Prompting)

Multi-Query

Pre-defined Query

Structure

(e.g., ToT, GoT)

Output

N-hop Iteration

Problem

Distiller

Thought Retrieval

Instantiated

Reasoning

Buffer of Thoughts (BoT)

Output

Thought Distillation

and Update

High-level

Thoughts

Efficiency

&

Efficiency

Reasoning

Thought

Expansion

Input query

Input query

expansion of reasoning paths, multi-query reasoning is usually computationally-intensive when finding a unique intrinsic structure underlying the reasoning process for each specific task; (3) Both single-query and multi-query reasoning processes are limited by their designed exemplars and reasoning structures, and they neglect to derive general and high-level guidelines or thoughts from previously-completed tasks, which are informative for improving efficiency and accuracy when solving similar problems.

To address these limitations, we propose Buffer of Thoughts (BoT), a novel and versatile thoughtaugmented reasoning framework aimed at enhancing reasoning accuracy, efficiency and robustness of LLMs across various tasks. Specifically, we design meta-buffer , a lightweight library housing a series of universal high-level thoughts ( thought-template ), which are distilled from different problem-solving processes and can be shared across tasks. Then, for each problem, we retrieve a relevant thoughttemplate and instantiate it with specific reasoning structure for efficient thought-augmented reasoning. In order to guarantee the scalability and stability of our BoT, we further propose buffer-manager to dynamically update the meta-buffer, which effectively enhances the capacity of meta-buffer as more tasks are solved.

Our method has three critical advantages: (i) Accuracy Improvement: With the shared thoughttemplates, we can adaptively instantiate high-level thoughts for addressing different tasks, eliminating the need to build reasoning structures from scratch, thereby improving reasoning accuracy. (ii) Reasoning Efficiency: Our thought-augmented reasoning could directly leverage informative historical reasoning structures to conduct reasoning without complex multi-query processes, thus improving reasoning efficiency. (iii) Model Robustness: The procedure from thought retrieval to thought instantiation is just like the human thought process, enabling LLMs to address similar problems in a consistent way, thus significantly enhancing the model robustness of our method. Our empirical studies demonstrate that Buffer of Thoughts significantly improves precision, efficiency, and robustness over a diverse array of tasks. Here, we summarize our contributions as follows:

We propose a novel thought-augmented reasoning framework Buffer of Thoughts (BoT) for improving the accuracy, efficiency and robustness of LLM-based reasoning.

We propose meta-buffer for store informative high-level thoughts distilled from different problems, and adaptively instantiate each thought template to address each specific task.

We design buffer-manager to distill thought-templates from various solutions, and is continually improves the capacity of meta-buffer as more tasks are solved.

We conduct extensive experiments on 10 challenging reasoning-intensive tasks. Our BoT achieves significant performance improvements over previous SOTA methods: 11% on Game of 24, 20% on Geometric Shapes and 51% on Checkmate-in-One , while requiring only 12% of the cost of multi-query prompting methods on average.

2 Related Work and Discussions

Retrieval-Augmented Language Models The retrieval-augmented (Large) Language Model is introduced as a solution to mitigate the phenomenon of hallucination and enhance the output quality of language models [18-22]. When presented with an input question, the retrieval-augmented LLM first queries an external database with billion-level tokens [23] for retrieving a subset of the text corpus to help generating the final answer. Notably, the retrieval-augmented LLM achieves superior question-answering performance using fewer parameters compared to conventional LLMs [19], and it has found application across various downstream tasks [24-26], including multi-modal generation [24, 22, 23, 25] and biomedical applications [26, 27]. In this paper, we construct a novel category of retrieval database, termed meta-buffer , which contains a series of high-level thoughts rather than specific instances, aiming to universally address various tasks for LLM-based reasoning.

Prompt-based Reasoning with Large Language Models Prompting techniques have significantly enahnced the arithmetic and commonsense reasoning capabilities of LLMs. Chain-of-Thought (CoT) prompting [8] and its variants [28-30], such as Least-to-Most [16], Decomposed Prompting [31], and Auto-CoT [13]-prompt LLMs to break down complex questions into simpler subtasks and systematically solve them before summarizing a final answer. Numerous studies [32-37] have demonstrated the effectiveness of these prompting methods across a wide range of tasks and benchmarks. Innovations like Tree-of-Thought [14] and Graph-of-Thought [17], have further advanced this field by exploring dynamic, non-linear reasoning pathways to expand heuristic capabilities of LLMs [38, 39]. However, they suffer from increased resource demands and greater time complexity, depend on manual prompt crafting, and are often tailored to specific task types. Recent meta prompting methods [15, 40] utilize a same task-agnostic form of prompting for various tasks and recursively guide a single LLM to adaptively addressing different input queries. Nevertheless, such a long meta prompt may require a considerable context window, and these methods fail to leverage historical informative guidelines or thoughts for potential similar tasks.

Analogical Reasoning Analogical reasoning is a useful technique for natural language reasoning [41-45]. Recent works demonstrate that LLMs can perform analogical reasoning just like humans [46, 47, 12, 48, 49]. For example, Analogical Prompting [12] and Thought Propagation [48] prompt LLMs to self-generate a set of analogous problems, and then utilize the results of analogous problems to produce a solution for input problem. However, the specific solutions for self-explored problems may introduce additional noise and cause error accumulation. Recent Thought-Retriever [49] uses the intermediate thoughts generated when solving past user to address analogous queries, but it only focuses on textual comprehension/generation instead of general reasoning problems. Thus, a more high-level and general analogical approach for LLM complex reasoning is still lacking.

3 Buffer of Thoughts

Overview of Buffer of Thoughts In this section, we introduce our Buffer of Thoughts in detail and we also illustrate our core thought-augmented reasoning process in Figure 2. Given a specific task, we utilize our problem-distiller (Section 3.1) to extract critical task-specific information along with relevant constraints. Based on the distilled information, we search in meta-buffer (Section 3.2) that contains a series of high-level thoughts ( thought-template ) and retrieve a most relevant thoughttemplate for the task. Subsequently, we instantiate the retrieved thought-template with more taskspecific reasoning structures and conduct reasoning process. Finally, we employs a buffer-manager (Section 3.3) for summarizing the whole problem-solving process and distilling high-level thoughts for imcreasing the capacity of meta-buffer.

3.1 Problem Distiller

Most of complex tasks contain implicit constraints, complex object relationships, and intricate variables and parameters within their contexts. Consequently, during the reasoning stage, LLMs need to overcome three main challenges: extracting vital information, recognizing potential constraints, and performing accurate reasoning. These challenges would impose a significant burden on a single LLM. Therefore, we separate the extraction and comprehension stages of task information from the final reasoning stage, through prepending a problem distiller to the reasoning process. More

To solve any quadratic equation of the form

!

+	𝑏𝑥	 + 	𝑐	 = 	0

, we can follow a general

approach based on the method described. Here

is the structured template for solving such

equations:

Step 1: Calculate the Discriminant

•

Compute the discriminant

𝐷

using the formula

𝐷

=

𝑏

!

-4𝑎𝑐

.

Step 2: Determine the Nature of the Roots

•

If

𝐷≥

0, the equation has two distinct real roots.

•

If

𝐷

=0, the equation has exactly one real root

(also known as a repeated or double root).

•

If

𝐷

<0, the equation has two complex roots.

Step 3: Compute the Roots

•

For

𝐷

≥0, calculate the roots using the formula

𝑥 =

"#± %

!&

For

𝐷

<0, calculate the real and imaginary parts

of the complex roots using the formula

𝑥 =

"#± "%'

!&

where

𝑖

is the imaginary unit.

……….

Thought Template

𝑻

𝟏

Solution:

•

𝑝

be the original profit per shirt (40 yuan).

•

𝑥

be the amount by which the price of each shirt is

reduced.

•

𝑞

be the original quantity of shirts sold daily (20 pieces).

•

𝑟

be the rate at which the quantity of shirts sold increases.

The mall wants to maintain an average daily profit of

1200 yuan, so we have:

……

After simplification

：

𝒙

𝟐

-𝟑𝟎𝒙 + 𝟐𝟎𝟎 = 𝟎

Now we can solve this quadratic equation using the

steps outlined in the template:

Step 1: Calculate the Discriminant

𝐷

=

𝒃

𝟐

-𝟒𝒂𝒄

𝑫 = (-𝟑𝟎)

𝟐

-𝟒 ∗ 𝟏 ∗ 𝟐𝟎𝟎

=100

Step 2: Determine the Nature of the Roots

Since

𝐷

>=0, the equation has two distinct real roots.

Step 3: Compute the Roots

𝒙 =

"𝒃± 𝑫

𝟐𝒂

=

𝟐

The two possible solutions are:

𝑥

=20 or x =10

Due to reducing inventory as soon as possible, x=20 is

taken

Instantiated

Reasoning

To devise a plan to solve the problem, let's break it

down into steps:

1.Determine the current daily profit:

Calculate the current daily profit using the average daily

sales and profit per shirt

…….

3. Set up the equation for the desired daily profit

Create an equation that relates new profit per shirt,

increased number of shirts sold, and desired daily profit

……

Step 4: Solve the equation

Expand the equation and collect like terms:

1200=(20

×

40)+(2

𝑝

×

40)-(20

×

𝑝

)-(2

𝑝

×

𝑝

)

Divide by 2 to simplify:

𝒑

𝟐

-30

𝑝

-200 = 0

p≈35.6155

Plan-and-Solve

1.Calculate the current daily profit:

Current daily profit = 20 * 40 = 800 yuan

2.Calculate additional daily profit from selling more shirts:

Additional daily profit = 2 * 40 = 80 yuan

……

5. Calculate Price reduction:

Price reduction needed = 320 / 2 = 160 yuan

Therefore, each shirt should be reduced by 160 yuan in

order for the mall to make an average profit of 1200

yuan per day.

Chain-of-Thought

Buffer of Thoughts

To solve any quadratic equation of the form

!

+	𝑏𝑥	 + 	𝑐	 = 	0

, we can follow a general

approach based on the method described. Here is

the structured template for solving such equations:

Step 1: Calculate the Discriminant

•

Compute the discriminant

𝐷

using the formula

𝐷

=

𝑏

!

-4𝑎𝑐

.

Step 2: Determine the Nature of the Roots

……

Thought Template

𝑻

𝟏

def process_element(element):

# Define how to process each individual element,

e.g., applying a filter or transformation.

def combine_elements(element1, element2):

# Define how to combine elements, e.g.,

summing numbers, concatenating strings, etc.

def check_condition(accumulated_result):

# Define the condition that the accumulated

result must meet.

def solve_problem(input_list):.

Thought Template

𝑻

𝑵

A certain shopping mall sells a batch of branded shirts, with an average daily sales of 20 pieces and a profit of 40 yuan per piece. In order to expand sales, increase profits, and

reduce inventory as soon as possible, the mall has decided to take appropriate price reduction measures. After investigation, it was found that for every 1 yuan decrease in the price

of this shirt, an average of 2 more shirts are sold per day. If the mall wants to make an average profit of 1200 yuan per day, how much price should each shirt be reduced?

……

Problem Distillation

&

Thought Retrieval

concretely, we design a meta prompt ϕ to first distill and formalize the task information. The distilled task information could be denoted as:

where x is the task statement. Due to the page limit, we put the detailed meta prompt for problemdistiller in Appendix B.2.

Problem Condensation and Translation We use the problem distiller to extract key elements from input tasks, focusing on: (1). Essential parameters and variables for problem-solving; (2). The objectives of the input tasks and their corresponding constraints. We then re-organize this distilled information into a clear, comprehensible format for the subsequent reasoning stage. We then translate the specific problems into high-level concepts and structures. This translation procedure decomposes complex real-world problems, like intricate mathematical application scenarios, into simpler, multi-step calculations, making it easier for later retrieval of high-level thought.

3.2 Thought-Augmented Reasoning with Meta Buffer

Motivation Human often summarize and induce higher-level guidelines when solving problems and then apply them to relevant problems. Motivated by this, we propose meta-buffer , a lightweight library that contains a series of high-level thoughts ( thought-template ) for addressing various types of problems. Unlike traditional methods [11, 46, 12, 36, 9] that require specific instructions or exemplars, our high-level thought-templates can be adaptively instantiated when solving different problems, thereby enhancing LLMs with superior precision and flexibility.

Thought Template As a kind of high-level guideline, our thought-template is stored in metabuffer , and is obtained from various problem-solving processes by our buffer-manager . The details about acquiring thought-templates would be introduced in Section 3.3. Since our BoT aims to provide a general reasoning approach for various tasks, we correspondingly classify the thoughttemplates into six categories: Text Comprehension, Creative Language Generation, Common Sense Reasoning, Mathematical Reasoning, Code Programming and Application Scheduling. We provide some example thought-templates in Appendix B.1. Such classification of thought-templates can

facilitate the template retrieval for finding most suitable solutions to different problems. Here we denote thought template, template description and its corresponding category as ( T i , D T i , C k ) , where i denotes the index of meta-template, k ∈ Z + and 1 ≤ k ≤ 6 , which means C k is in one of the six categories, and D T i is the description of thought template.

Template Retrieval For each task, our BoT retrieves a thought-template T i that is highly similar to the distilled problem x d by calculating the embedding similarity between the description D T i and x d . The retrieval process can be formulated as:

N is the size of the meta-buffer, f ( · ) is a normal text embedding model, and T j denotes the retrieved thought template. We set a threshold δ (0.5 ∼ 0.7 is recommended) to determine whether the current task is new. Therefore, if Sim ( f ( x d ) , { f ( D T i ) } n i =0 ) < δ , we identify the task x as a new task.

Instantiated Reasoning For each specific task, we discuss two situations for the instantiated reasoning, depending on whether the current task is new: The first situation is that we successfully retrieve a thought-template T j for the task. In this case, as presented in Figure 2, our thoughtaugmented reasoning will be adaptively instantiated to suitable reasoning structures with our designed instantiation prompt (in Appendix B.3). For example, in a Checkmate-in-One problem, we instantiate the template of updating chess board state to solve the problem step by step. Thus we conduct the instantiated reasoning for task x using the distilled information x d and the retrieved template T j , and produce its solution S x as:

where LLM instantiation denotes the instantiated reasoner with a LLM.

In the second situation, the task is identified as a new task. To enable proper instantiated reasoning, we prepare three general coarse-grained thought-templates for utilization. Based on the distilled task information x d , our BoT would automatically assign a suitable thought-template to the reasoning process. The detailed pre-defined thought-templates are included in Appendix B.3).

3.3 Buffer Manager

We propose buffer-manager to summarize the high-level guidelines and thoughts that are gained from each problem-solving process. It can generalize each specific solution to more problems, storing the critical distilled knowledge in the form of thought-templates within the meta buffer. In contrast to methods that temporarily generate exemplars or instructions for each problem, our buffer-manager can ensure permanent advancements in accuracy, efficiency, and robustness for LLM-based reasoning.

Template Distillation To extract a general though-template, we propose a three-step approach: (1) Core task summarization: identifying and describing basic types and core challenges of problems; (2) Solution steps description: summarize the general steps for solving a problem; (3) General answering template: based on the above analysis, propose a solution template or approach that can be widely applied to similar problems. Additionally, to boost the generalization ability and stability of template distillation, we carefully design two types of in-context examples of how to generate thoughttemplatein-task and cross-task examples. Cross-task means we choose the template distilled from one task to tackle the problem of other tasks, such as addressing a mathematical problem with a code-related thought-template. The new template distilled from input task x can be denoted as:

where LLM distill is the LLM-based template distiller initialized with the following prompt:

Prompt for Template Distillation:

User: [Problem Description] + [Solution Steps or Code]

To extract and summarize the high-level paradigms and general approaches for solving such problems, please follow these steps in your response:

1. Core task summarization:

Identify and describe the basic type and core challenges of the problem, such as classifying it as a mathematical problem (e.g., solving a quadratic equation), a data structure problem (e.g., array sorting), an algorithm problem (e.g., search algorithms), etc. And analyze the most efficient way to solve the problem.

2. Solution Steps Description:

Outline the general solution steps, including how to define the problem, determine variables, list key equations or constraints, choose appropriate solving strategies and methods, and how to verify the correctness of the results.

3. General Answer Template:

Based on the above analysis, propose a template or approach that can be widely applied to this type of problem, including possible variables, functions, class definitions, etc. If it is a programming problem, provide a set of base classes and interfaces that can be used to construct solutions to specific problems.

Please ensure that your response is highly concise and structured, so that specific solutions can be transformed into generalizable methods.

[Optional] Here are some exemplars of the thought-template: (Choose cross-task or in-task exemplars based on the analysis of the Core task summarization .)

Dynamic Update of Meta-Buffer After template distillation, we need to consider whether the distilled template should be updated into the meta-buffer. If we initialize an empty meta-buffer or encounter a problem without a proper thought-template, the distilled thought-templates will be directly stored in the meta-buffer. If we solve problem with a retrieved thought-template, new insights may arise during the instantiation of a certain thought-template. Therefore, to avoid the redundancy of the meta-buffer while maintaining newly-generated informative thoughts, we will calculate the similarity between the embedding vectors of D T new and { D T i } n i =0 and update the meta-buffer with the following rule:

Otherwise, it means the meta-buffer has already possessed the necessary knowledge to solve this task and does not need to perform the update. Our dynamic update strategy effectively reduces the computational burden of template retrieval while ensuring the lightweight property of our meta-buffer. We further conduct ablation study to analyze it in Section 4 and Appendix A.

4 Experiments

Datasets and Tasks To evaluate the efficacy of our proposed Buffer of Thoughts and compare with previous methods, we consider a diverse set of tasks and datasets that require varying degrees of mathematical and algorithmic reasoning, domain-specific knowledge, and literary creativity: (a). The Game of 24 from ToT [14], where the objective is to form an arithmetic expression that equals 24 using each of four given numbers exactly once; (b). Three BIG-Bench Hard (BBH) [35] tasks: Geometric Shapes , Multi-Step Arithmetic Two , and Word Sorting ; (c). Three reasoning tasks directly obtained from the BIG-Bench suite [50]: Checkmate-in-One , Penguins -where the task is to answer questions about penguins' attributes based on a given table and additional natural language information, and DateUnderstanding -a task that involves inferring dates from natural language descriptions, performing arithmetic operations on dates, and utilizing global knowledge such as the number of days in February; (d). Python Programming Puzzles (P3) [51, 52], a collection of challenging programming puzzles written in Python with varying difficulty levels; (e). Multilingual Grade School Math (MGSM) [33], a multilingual version of the GSM8K dataset [53] featuring translations of a subset of examples into ten typologically diverse languages, including Bengali, Japanese, and Swahili; (f). Shakespearean Sonnet Writing from meta-prompting [15], a novel task where the goal is to write a sonnet following the strict rhyme scheme "ABAB CDCD EFEF GG" and incorporating three provided words verbatim.

Implementation and Baselines For the fair comparisons with previous methods, we use GPT-4 as the base model of our BoT, including the main experiment and the ablation study. We also use Llama3-8B and Llama3-70B in our analysis part on NVIDIA A100-PCIE-40GB GPU. We compare our Buffer of Thoughts with the following prompting methods: 1. Standard Prompting : This is our most basic baseline, where an LLM is asked to generate a response directly from the input query, without any specific guiding input-output examples or additional instructions beyond the task description included in the query.

2. Single-query Method : This includes Zero-shot CoT [8] and PAL [10], which use the LLM to analyze natural language problems and generate intermediate reasoning steps. We also include Expert Prompting [9], which creates an expert identity tailored to the specific context of the input query, and then integrates this expert profile into the input to generate a well-informed response.

3. Multi-query Method : This includes ToT [14] and GoT [17], which enable LLMs to make deliberate decisions by considering multiple reasoning paths and self-evaluating choices to determine the next course of action. These methods also allow for looking ahead or backtracking when necessary to make global decisions. Additionally, we include Meta Prompting [15], which employs an effective scaffolding technique designed to enhance the functionality of LLMs.

4.1 BoT Achieves Better Accuracy, Efficiency and Robustness

Reasoning Accuracy As shown in Table 1, our BoT consistently outperforms all previous prompting methods across multiple kinds of challenging benchmarks, particularly demonstrated in complicated reasoning tasks such as Game of 24 and Checkmate-in-One. Taking GPT-4 as a baseline, our method achieves an astonishing 79.4% accuracy improvement in Game of 24, and compared to ToT, which has a good performance on this task, we also achieve an 8.4% accuracy improvement. What's more, compared to recent Meta-prompting method [15], we see significant accuracy improvements: 23% on Game of 24, 20% on Geometric Shapes and 51% on Checkmate-in-One . Existing methods need complex, iterative, and heuristic search strategies to address these problems on a case-by-case basis. Conversely, our BoT leverages the historical insights and informative guidelines from thought-templates, and further adaptively instantiate a more optimal reasoning structure for addressing these complex problems.

Reasoning Efficiency In addition to significant improvements in accuracy, as a multi-query method, our BoT can achieve comparable reasoning time to single-query method across various tasks, while being considerably less than conventional multi-query method like ToT [14] as shown in Figure 3. For example, in Game of 24, both single-query and multi-query methods necessitate iterative and heuristic searches to identify feasible solutions. This process is particularly time-consuming and inefficient, especially for the multi-query method, which involves conducting multi-query search and backtrace phases. In contrast, our BoT directly retrieves a thought-template in code format, thus a program is instantiated to traverse combinations of numbers and symbols, thereby eliminating the need to build the reasoning structure from scratch. This allows for solving the problem with just one query after invoking the problem-distiller, significantly reducing the time required for complex reasoning. Notably, our BoT requires only 12% of the cost of multi-query methods (e.g., tree of thoughts and meta-prompting) on average.

4.64

4.16

4.81

5.5

4.81

5.21

8.73

8.34

9.03

8.47

8.04

8.43

5.17

5

6.39

0

1

2

3

4

5

6

7

8

9

10

Game of 24

MGSM

Checkmate-in-One

Logarithmic time (s)

Comparison of the inference time

Expert

PAL

ToT

Meta-prompting

Ours

Success rate

27

85

48.2

67.13

36

87

53.4

71.82

61

76

36.4

70.12

71

84

78.4

84.57

98

96.8

93.4

95.15

0

10

20

30

40

50

60

70

80

90

100

Game of 24

MGSM

Checkmate-in-One

Average

Average accuracy (%)

GPT4

Expert

PAL

ToT

Ours

Reasoning Robustness To better evaluate our BoT, we devise a new evaluation metric: success rate , which is used to assess the reasoning robustness. We randomly sample 1000 examples from various benchmarks as a test subset and evaluate different methods on this subset. As shown in Figure 4, we repeat this evaluation process 10 times and take the average accuracy as the success rate of different methods on each benchmark. Compared with other methods, our BoT consistently maintains a higher success rate across various tasks, surpassing the second-best by 10% in average success rate. We attribute our outstanding robustness to the great generalization ability of our distilled thought-templates during reasoning across different tasks. By offering high-level thought from the suitable thought-templates, the stability of our method across different tasks is greatly enhanced.

5 Model Analysis

Distribution Analysis of Thought-Templates As depicted in the left figure of Figure 5, we choose six different benchmarks, each sampled with 100 distinct tasks. We update the meta-buffer from scratch, and after completing all sampled tasks, we display the number of derived thought-templates. We can observe that our BoT generates a greater number of thought-templates in the MGSM tasks that contain more diverse scenarios. In tasks with relatively simple requirements, such as Checkmatein-One and Penguins, BoT produces more fixed thought-templates tailored for those specific issues. The distribution of templates indicates that our BoT can effectively discover appropriate thought templates for different benchmarks.

Templatedistributionacrossdifferenttasks

AveragetimedistributionforeachpartofourBoT

Checkmate-in-One

Python Puzzles

37

4

37

Sonnet writing

buffer-manager

21.3

15.6

problem-distiller

Table of Penguins

8

meta-buffer

Date understand...

8.9

14

Sonnet writing

MGSM

Table of Penguins

reasoner

78

Date understanding

52.7

problem-distiller

MGSM

reasoner

Python Puzzles

meta-buffer

Checkmate-in-One

buffer-manager

Distribution Analysis of Time Cost As illustrated in Figure 5, we measured the average time cost for each component of BoT's reasoning framework across different tasks. The time required for distilling task information and template retrieval is relatively short, whereas instantiated reasoning takes longer. Overall, considering the complexity of different components, our BoT achieves a relatively balanced distribution of time cost, demonstrating the efficiency of our BoT framework.

Better Trade-off between Model Size and Performance As depicted in Figure 6, on Game of 24, word list sorting and Checkmate-in-One, Llama3-8B and Llama-70B models [6] may result in poor outcomes. However, equipped with our BoT, both models demonstrate a substantial accuracy improvement. Notably, BoT+Llama3-8B has the potential to surpass single Llama3-70B model . Our BoT enables smaller models to exhibit the capabilities that approximate or even surpass larger models, significantly bridging the gap between their reasoning abilities. Furthermore, it greatly diminishes the inference cost required by large language models when tackling complex problems.

Trade-off between model size and performance

1.2

48.4

0.8

2.4

79

15

73.4

73.4

56.7

78.4

92.3

75.6

0

10

20

30

40

50

60

70

80

90

100

Game of 24

Word list sorting

Checkmate-in-One

Accuracy (%)

BoT+Llama-3-70B

BoT+Llama-3-8B

Llama3-70B

Llama3-8B

Quanlity of Automatically-Induced Template The success of the proposed approach critically depends on the quality of the automatically induced template. While this previous experiments has shown promising empirical performance on downstream tasks, it remains unclear how good the templates themselves are. Thus we make a comparison between the automatically generated task templates with manually prepared templates for more complex reasoning tasks on MATH dataset [54], with randomly sampled 500 problems. From the results, we can find that our automatic template boosts the reasoning ability of LLMs, demonstrating its generalization ability.

Impact of Buffer-Manager We further conduct ablation study on our buffer-manager, where we divide the entire process into four rounds. In each round, we randomly sample 50 questions from

each benchmark and conduct reasoning. In the subsequent round, we continue to randomly sample another 50 questions from each benchmark. As depicted in Figure 7, with the increase of the number of rounds, the model with the buffer-manager continually expands the meta-buffer while also utilizing the thought-templates obtained from previously solved problems to help addressing subsequent similar problems. Therefore, we can observe that the accuracy of BoT steadily improves with each round. In contrast, the model without the buffer-manager fails to exhibit an upward trend.

Additionally, we also demonstrate the superiority of our buffer-manager on the reasoning efficiency as depicted in Figure 10. when the number of rounds increases, the model with the buffer-manager will experience a continual improvement in reasoning efficiency. This is because, with the continual expansion of the meta-buffer, the likelihood of retrieving suitable thought-templates also increases. Consequently, models can avoid constructing reasoning structures from scratch, thereby enhancing the inference efficiency accordingly. More ablation study can be found in Appendix A.

56.8

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Ablation study of buffer-manager -- Accuracy

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BoT+GPT4 (w/o buffer-manager)

6 Discussion

In this work, we introduce Buffer of Thoughts, a novel beffered reasoning framework that employs LLMs to utilize pre-accumulated experiences and methodologies from prior tasks for progressively raising the LLM's reasoning capacity. our BoT brings out a set of future directions: (1). integrating external resources with BoT to build a open-domain system like agent models [55, 56]. (2). making the distillation of thought-templates optimizable, which may significantly enhance their template qualities for more complex tasks. (3). incorporating BoT into LLM training for eliciting more fine-grained and accurate reasoning process, like SuperCorrect [57].

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A More Ablation Studies

A.1 Impact of Problem-Distiller

As illustrated in Figure 8, when the problem-distiller is disabled, both Llama3-70B and GPT-4 experience a certain degree of accuracy decline. More complex problems, such as Game of 24 and Checkmate-in-One, show a more significant accuracy reduction, whereas relatively simpler problems like word list sorting and MGSM exhibit smaller decreases. This is because LLMs can more easily extract key information in simpler tasks, making the impact of the problem-distiller less noticeable. In contrast, extracting key information and potential constraints in complex problems is more challenging, making the role of our problem-distiller more prominent, thereby explaining the differences depicted in the figure.

Ablation study of problem-distiller

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BoT+GPT-4

A.2 Impact of Meta-Buffer

As illustrated in Figure 9, when the meta-buffer is disabled, both Llama3-70B and GPT-4 models exhibit a noticeable decline in performance, particularly in benchmarks requiring complex reasoning, such as Game of 24 and Checkmate-in-One. This further underscores the superiority of our metabuffer in addressing complex problems. We would extend such mechanism to more scenarios [57-61].

Ablation study of meta-buffer

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B Additional Method Details

B.1 Six Kinds of Detailed Thought-Templates

1. Text Comprehension

Task Description:

The task involves analyzing a table with various attributes of penguins, such as name, age, height, and weight, and answering questions about these attributes. The table may be updated with new entries, and additional context or comparisons may be provided in natural language.

Solution Description:

To accurately answer questions about the penguins' attributes, one must be able to interpret the data presented in tabular form, understand any additional information provided in natural language, and apply logical reasoning to identify the correct attribute based on the question asked.

Thought Template:

Step 1: Parse the initial table, extracting the header information and each penguin's attributes into a structured format (e.g., a list of dictionaries).

Step 2: Read and integrate any additional natural language information that updates or adds to the table, ensuring the data remains consistent.

Step 3: Identify the attribute in question (e.g., oldest penguin, heaviest penguin) and the corresponding column in the table.

Step 4: Apply logical reasoning to compare the relevant attribute across all entries to find the correct answer (e.g., the highest age for the oldest penguin).

Step 5: Select the answer from the provided options that matches the result of the logical comparison.

2. Creative Language Generation

Task Description:

The task is to generate a sonnet that adheres to the traditional English sonnet rhyme scheme of "ABAB CDCD EFEF GG" and includes three specific words verbatim in the text.

Solution Description:

Writing a sonnet involves crafting 14 lines of poetry that follow a specific rhyme pattern. The lines are typically in iambic pentameter, though flexibility in rhythm can be allowed for creative reasons. The given rhyme scheme dictates the end sounds of each line, ensuring a structured poetic form. Incorporating the three provided words verbatim requires strategic placement within the lines to maintain the poem's coherence and thematic unity.

Thought Template:

Step 1: Identify the three words that must be included in the sonnet.

Step 2: Understand the rhyme scheme "ABAB CDCD EFEF GG" and prepare a list of rhyming words that could be used.

Step 3: Develop a theme or story for the sonnet that can naturally incorporate the three provided words.

Step 4: Begin drafting the sonnet by writing the first quatrain (four lines) following the "ABAB" rhyme scheme, ensuring one or more of the provided words are included.

Step 5: Continue with the second quatrain "CDCD," the third quatrain "EFEF," and finally the closing couplet "GG," each time incorporating the provided words as needed.

Step 6: Review the sonnet for coherence, flow, and adherence to the rhyme scheme, making adjustments as necessary.

3. Common Sense Reasoning

Task Description:

Given a specific date and an event, such as a holiday or historical event, determine the following date.

Solution Description:

To determine the next date, we need to consider the structure of the calendar, the number of days in each month, and whether it's a leap year. Typically, the number of days in a month is fixed, except February may vary due to leap years. The next day in a year is usually the date increased by one day unless it's the end of the month, then the next day will be the first day of the following month. For the end of the year, the next day will be January 1st of the following year.

Thought Template:

Step 1: Identify the given date's month and day number.

Step 2: Check if it's the end of the month; if so, confirm the start date of the next month.

Step 3: If it's not the end of the month, simply add one to the day number.

Step 4: Pay special attention to the end of the year, ensuring the year increments.

4. Code Programming

Task Description:

When given a list of numbers, try to utilize 4 basic mathematical operations (+-*/) to get a target number.

Thought Template:

5. Application Scheduling

6. Mathematical Reasoning

Task Description:

Solve an quadratic equation of the form ax 2 + bx + c = 0 considering any situations.

Solution Description:

To solve any quadratic equation of the form ax 2 + bx + c = 0 , we can follow a general approach based on the method described. Here is the structured template for solving such equations:

Thought Template:

Step 1: Calculate the Discriminant

- Compute the discriminant D using the formula D = b 2 -4 ac .

Step 2: Determine the Nature of the Roots

If D > 0 , the equation has two distinct real roots.

- If D = 0 , the equation has exactly one real root (also known as a repeated or double root).

- If D < 0 , the equation has two complex roots.

- For D < 0 , calculate the real and imaginary parts of the complex roots using the formula x = -b 2 a ± √ -D 2 a i , where i is the imaginary unit.

Step 3: Compute the Roots - For D ≥ 0 , calculate the roots using the formula x = -b ± √ D 2 a .

B.2 Prompt for Problem Distiller

[Problem Distiller] :

As a highly professional and intelligent expert in information distillation, you excel at extracting essential information to solve problems from user input queries. You adeptly transform this extracted information into a suitable format based on the respective type of the issue.

Please categorize and extract the crucial information required to solve the problem from the user's input query, the distilled information should include.

1. Key information:

Values and information of key variables extracted from user input, which will be handed over to the respective expert for task resolution, ensuring all essential information required to solve the problem is provided.

2. Restrictions:

The objective of the problem and corresponding constraints.

3. Distilled task:

Extend the problem based on 1 and 2, summarize a meta problem that can address the user query and handle more input and output variations. Incorporate the real-world scenario of the extended problem along with the types of key variables and information constraints from the original problem to restrict the key variables in the extended problem. After that, use the user query input key information as input to solve the problem as an example.

B.3 Prompt for Instantiated Reasoning

[Meta Reasoner]

You are a Meta Reasoner who are extremely knowledgeable in all kinds of fields including Computer Science, Math, Physics, Literature, History, Chemistry, Logical reasoning, Culture, Language..... You are also able to find different high-level thought for different tasks. Here are three reasoning sturctures:

i) Prompt-based structure:

It has a good performance when dealing with problems like Common Sense Reasoning, Application Scheduling

ii) Procedure-based structure

It has a good performance when dealing with creative tasks like Creative Language Generation, and Text Comprehension

iii) Programming-based:

It has a good performance when dealing with Mathematical Reasoning and Code Programming, it can also transform real-world problems into programming problem which could be solved efficiently.

( Reasoning instantiation )

Your task is:

1. Deliberately consider the context and the problem within the distilled respond from problem distiller and use your understanding of the question within the distilled respond to find a domain expert who are suitable to solve the problem.

2. Consider the distilled information, choose one reasoning structures for the problem.

3. If the thought-template is provided, directly follow the thought-template to instantiate for the given problem.

NeurIPS Paper Checklist

1. Claims

Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?

Answer: [Yes]

Justification: Please refer to the abstract in the main paper.

Guidelines:

The answer NA means that the abstract and introduction do not include the claims made in the paper.

The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No or NA answer to this question will not be perceived well by the reviewers.

The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings.

It is fine to include aspirational goals as motivation as long as it is clear that these goals are not attained by the paper.

2. Limitations

Question: Does the paper discuss the limitations of the work performed by the authors?

Answer: [Yes]

Justification: Please refer to the Section 6 in the paper.

Guidelines:

The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.

The authors are encouraged to create a separate "Limitations" section in their paper.

The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g., independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect on how these assumptions might be violated in practice and what the implications would be.

The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on a few datasets or with a few runs. In general, empirical results often depend on implicit assumptions, which should be articulated.

The authors should reflect on the factors that influence the performance of the approach. For example, a facial recognition algorithm may perform poorly when image resolution is low or images are taken in low lighting. Or a speech-to-text system might not be used reliably to provide closed captions for online lectures because it fails to handle technical jargon.

The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size.

If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairness.

While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, a worse outcome might be that reviewers discover limitations that aren't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an important role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations.

3. Theory Assumptions and Proofs

Question: For each theoretical result, does the paper provide the full set of assumptions and a complete (and correct) proof?

Answer: [NA]

Justification: The paper does not include theoretical results.

Guidelines:

The answer NA means that the paper does not include theoretical results.

All the theorems, formulas, and proofs in the paper should be numbered and crossreferenced.

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Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors propose a new framework to optimize prompting for various tasks. The frameworks consists of a buffer, where high-level problem-solving templates are stored. A query is first processed by extracting its key aspects.Templates are selected using embeddings of the templates and the extracted key aspects query. The high-level problem-solving templates is then instantiated with the extracted key aspects of the query and its inference is executed. Additionally a buffer manager is introduced to allow updating of the thought template buffer with new, \"specialized\", thought templates.\n\nSTRENGTHS:\nThe paper combines the idea of RAG with a library of templates for solving tasks in order to make automatic prompt engineering more flexible and by using a cache (the library) more efficient, since it avoids more complex exploration. The framework and its components are sufficiently outlined and their description is clear. The writing is easy to follow. The evaluation shows clear advantages over existing approaches, while being executed with modern LLMs.\n\nWEAKNESSES:\nClaims from the checklist:\n* point 6: It is not clear, what embedding model is used.\n* point 8: No sufficient information are provided for the compute resources, especially the local (?) Mixtral execution.\n\nNot clear, how well their approach scales with the number of different thought templates (and not just specialized one). Additionally it is not clear, how well their approach works, if the query (something completely different) does not match any of thought templates in the buffer, as at least some of thought templates seem to specifically designed for the respective evaluation tasks. This is also one of the weaker points, since the authors claim that their approach requires less manually labor from the user than other approaches.\n\nI would have appreciate some examples for the tasks in the appendix, so that it is not necessary to look them up in the respective code/papers.\n\nThe language can be polished a little bit more. For example:\n* lines 29/30: \"which makes it impractical to manually design them task by task\" - it is just cumbersome to manually design them, but not impossible\n* lines 69/70: \"the retrieval-augmented LLM first queries an external database with billion-level tokens [23] for retrieving a subset of the text\" - I think that is just how that particular paper implemented RAG, but it is not a general description of the idea behind RAG.\n* typos:\n  * \"enahnced\" (line 78)\n  * Llama-70B (line 277)\n\nreferences could also use a little but of work, for example:\n* reference [27]: no place of publication\n* references are slightly inconsistent:\n  * for example one time the abbreviation ICLR is used, otherwise not\n  * sometimes the number prefix (for example Eleventh) for conferences is used, sometimes not\n\nQUESTIONS:\n* What embedding model is used for the thought buffer?\n* Why it is too computationally expensive to provide error bars for the existing results? It should be possible to compute them locally at least for some of the tasks.\n* Figure 3:\n  * What is logarithmic time?\n  * Why is the inference time of BoT not at least twice that of the Expert baseline, since it needs at least two LLM interactions, where as the Expert baseline only requires one (if I understood that correctly)?\n\nLIMITATIONS:\nThe limitations are reasonably explained.", "TITLE": "Official Review", "RECOMMENDATION": 7, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-06-27"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a novel reasoning procedure for LLM, named \"buffer of thoughts\". The core idea is that for each task, first extract a template describing how the task should be solved, then store all these templates in a \"buffer\". As the LLM receives a new query, a retrieval procedure is applied to extract the most relevant task template out of the buffer. The template is then instantiated for this specific query, which becomes a concrete guideline that instructs the model how to solve the new query. The paper demonstrates that this procedure outperforms baselines like tree-of-thought and graph-of-thought significantly on some reasoning tasks.\n\nSTRENGTHS:\nThe novelty of the proposed method \"buffer of thought\" is a good contribution of the paper which may significantly improve LM's reasoning ability for some tasks.\n\nWEAKNESSES:\nGeneralizability and robustness of the proposed approach needs to be further verified, as the introduction of a hierarchical reasoning procedure (abstract template + instantiation) may create more noise and make it less reliable for novel tasks.\n\nQUESTIONS:\n1. The success of the proposed approach critically depends on the quality of the automatically induced template. While the paper showed empirical performance on downstream tasks, it remains unclear how good the templates themselves are. It would be great to make a comparison between the automatically generated task templates with manually prepared templates for some tasks.\n\n2. It remains unclear to me how the template for a specific task category (say maths) can potentially be revised and improved, as the model sees more and more examples from that task. Although the paper described briefly how the buffer can be dynamically updated (line 182), I don't quite understand how the template is updated automatically for higher accuracy as the model receives more examples (this seems to be a verbalized gradient descent procedure, but no quality improvement is guaranteed).\n\n3. What are the embedding models you used for retrieval purposes? Also it would be great to give some analysis on the impact of retrieval quality on downstream task performance, as finding the right template is critical.\n\n4. In Eq 5, it seems whether the template should be replaced should depend upon the template quality, instead of its similarity with the embedding vector.\n\n5. How quickly can this approach adapt to an unseen task? Would one or two examples from new task be enough to achieve reasonable accuracy? It would be great to give some analysis to the adaptability and continual learning ability of the proposed approach.\n\nLIMITATIONS:\nThe paper properly addressed limitations and impact of the work.", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-09"}

comments: SUMMARY:
This paper proposes a new approach called Buffer of Thoughts (BoT) to improve LLMs reasoning abilities. BoT addresses this by creating a "meta-buffer" that stores general problem-solving “thought” templates across different tasks. When a new query is given as input, BoT retrieves a relevant thought from the meta-buffer and tailors it to the specific situation. The method also includes a "buffer-manager" to keep the meta-buffer up-to-date and effective as the LLM encounters new challenges for scalability and stability purposes. The authors perform experiments on different reasoning tasks that showed significant improvements with BoT compared to previous methods.

STRENGTHS:
- The framework is clearly explained and the paper is easy to follow
- The empirical evaluation is thorough
- The improvement for some tasks is notable.
- The idea of a library of thoughts templates is sound and novel

WEAKNESSES:
- BoT seems to work well for problems with existing templates in the meta-buffer. However, entirely new or unique problems might not have a relevant template for BoT to adapt, potentially hindering its ability to solve them effectively.
- What happens in the case of unsolvable queries? Does the method allows for uncertainty based abstention?

QUESTIONS:
see weaknesses.

LIMITATIONS:
limitations have been discussed in the paper.
TITLE: Official Review
SOUNDNESS_CORRECTNESS: 3
DATE: 2024-07-11

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper introduces \"Buffer of Thoughts\" (BoT), a novel framework designed to improve the reasoning abilities of large language models (LLMs) by incorporating a 'thought-augmented' approach. This framework uses a component called 'meta-buffer' to store high-level thoughts—concise, distilled reasoning strategies from various problem-solving instances—which can be dynamically retrieved and adapted to new tasks to facilitate efficient reasoning. This approach significantly enhances the accuracy, efficiency, and robustness of LLMs across multiple reasoning-intensive tasks.\n1. Novel Framework: BoT innovates a thought-augmented reasoning mechanism that leverages previous problem-solving insights to aid new reasoning tasks, reducing the need for generating reasoning paths from scratch.\n2. Meta-Buffer: Introduces a storage system that holds distilled high-level thoughts, allowing for rapid adaptation to different tasks by retrieving and instantiating these thoughts as needed.\n3. Buffer Manager: A dynamic management system that updates the meta-buffer based on newly encountered tasks, continuously improving the system’s reasoning capacity.\n4. Empirical Validation: The paper reports extensive testing across 10 complex tasks, demonstrating significant performance improvements over state-of-the-art methods, including improvements of 11% on Game of 24, 20% on Geometric Shapes, and 51% on Checkmate-in-One, while also reducing the computational cost compared to traditional multi-query prompting methods.\n\nSTRENGTHS:\n1. **Improved Reasoning Accuracy:** BoT significantly enhances the reasoning accuracy of large language models by leveraging distilled high-level thoughts, allowing the models to approach problems with a pre-formed strategy that has been proven effective across tasks.\n2. **Computational Efficiency:** The thought-augmented reasoning approach minimizes the need for complex and iterative query processes typical in multi-query prompting systems. By reusing structured thought templates, BoT reduces the computational overhead, leading to faster reasoning times.\n3. **Robustness Across Tasks:** BoT exhibits a robust performance over a range of different and challenging tasks. This is attributed to the system's ability to adapt high-level reasoning thoughts to new problems, ensuring consistent performance without the need to tailor the system for specific tasks.\n\nWEAKNESSES:\n1. **Dependence on Quality of Distilled Thoughts:** The effectiveness of BoT hinges significantly on the quality of the distilled thoughts stored in the meta-buffer. If the initial thoughts distilled from problem-solving processes are not sufficiently generalizable or are too simplistic, they may not provide the necessary depth for complex reasoning tasks. \n2. **Scalability and Maintenance of the Meta-Buffer:** The paper introduces a dynamic system for updating the meta-buffer but does not deeply explore the long-term scalability and maintenance challenges associated with continuously growing and updating this repository. Managing an ever-expanding set of thought templates could lead to efficiency issues or dilution of useful thoughts.\n3. **Risk of Overfitting to Distilled Thoughts:** There is a potential risk that the LLM might overfit to the specific styles or patterns of reasoning encapsulated in the thought templates, especially if these templates are derived from a limited set of problem-solving instances.\n\nQUESTIONS:\n1. As the meta-buffer grows with more distilled thoughts, how does its size impact the retrieval time and overall performance of the model?\n2. In cases where multiple applicable thoughts could be retrieved from the meta-buffer, how does BoT prioritize or choose among conflicting reasoning strategies?\n3. Given the reliance on previously distilled thoughts, how does BoT handle situations where the foundational thoughts might be based on incorrect or outdated information?\n\nLIMITATIONS:\nOne of the primary limitations highlighted in the discussion of the Buffer of Thoughts (BoT) framework is its dependency on the initial quality of the meta-buffer. The performance of BoT is contingent upon the initialization of this buffer. If initialized with a weak model, the distilled thoughts stored in the meta-buffer may not be of sufficient quality or depth to facilitate effective reasoning across diverse tasks.", "TITLE": "Official Review", "RECOMMENDATION": 8, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-13"}