Chain of Thought Reranking

An approach to optimizing large language model (LLM) responses by extracting, reranking, and refining their internal chain-of-thought (CoT) reasoning.

Introduction to Chain of Thought Reasoning

Chain of Thought (CoT) is a prompting technique that encourages large language models to break down complex problems into intermediate steps before arriving at a final answer. This approach has been shown to significantly improve performance on tasks requiring multi-step reasoning, such as:

Mathematical problem-solving
Logical reasoning
Complex decision-making
Step-by-step analysis

While CoT has proven effective, the quality of reasoning can vary significantly between different attempts. This is where Chain of Thought Reranking comes in.

The Reranking Approach

Chain of Thought Reranking is a novel technique that:

Generates a detailed reasoning chain for a problem using a capable LLM
Extracts and segments the reasoning path into manageable chunks
Reranks the segments based on relevance to the original query
Selects the most relevant segments to create a refined prompt
Produces a final answer using a potentially smaller, more efficient LLM

Key Benefits

Improved Accuracy: By selecting the best reasoning segments, we can significantly reduce errors.
Enhanced Reliability: Focusing on the most relevant reasoning reduces the impact of occasional reasoning failures.
Greater Transparency: The process makes the model's reasoning explicit and reviewable.
Reduced Hallucinations: Filtering out less relevant segments helps identify and filter out inconsistencies.
Efficient Resource Usage: Smaller models can leverage the refined reasoning of larger models.

Implementation Details

The implementation involves several key components:

1. Initial Response Generation

We use a capable model like DeepSeek-R1 to generate a detailed chain-of-thought response:

cot_response = together_client.chat.completions.create(
    model='deepseek-ai/DeepSeek-R1',
    messages=[{'role': 'user', 'content': prompt}],
)

2. Chain Extraction and Segmentation

The reasoning chain is extracted from the response and split into manageable segments:

def split_into_sentences(text):
    pattern = r'(?<=[.!?])\s+'
    sentences = re.split(pattern, text.strip())
    return sentences

def group_sentences(sentences, group_size=4):
    return [' '.join(sentences[i:i+group_size]) for i in range(0, len(sentences), group_size)]

3. Reranking with Cohere

The segments are reranked based on their relevance to the original query:

reranking = co.rerank(
    model="rerank-v3.5", query=prompt, documents=chunks, top_n=5
)

4. Prompt Reconstruction

The top-ranked segments are formatted into a structured prompt:

def format_documents_into_prompt(documents):
    prompt_lines = ["Consider the following thoughts:\n"]
    for doc in sorted_docs:
        index = doc.index
        relevance = doc.relevance_score
        text = chunks[index]
        prompt_lines.append(f"Thought (index {index}, relevance: {relevance:.2%}):\n{text}\n")
    
    prompt = "\n".join(prompt_lines)
    return prompt

5. Final Answer Generation

A potentially smaller, more efficient model generates the final answer using the refined prompt:

final_response = together_client.chat.completions.create(
    model='meta-llama/Llama-3.2-3B-Instruct-Turbo',
    messages=[{'role': 'user', 'content': reranked_prompt}],
)

Experimental Results

In our experiments with a simple mathematical comparison problem ("Which number is larger, 9.9 or 9.11?"), we observed:

The base 3B model incorrectly answered that "9.11 is larger than 9.9"
After applying Chain of Thought Reranking:
- A larger model (DeepSeek-R1) provided detailed reasoning
- The reasoning was segmented and reranked
- The same 3B model, when given the reranked reasoning, correctly answered that "9.9 is larger than 9.11"

This demonstrates how smaller models can leverage the refined reasoning of larger models to produce more accurate results.

"The quality of your thinking determines the quality of your results." — This principle applies equally to humans and AI systems.

Challenges and Limitations

While promising, the approach faces several challenges:

Computational Overhead: The process requires multiple API calls and processing steps.
Dependency on Reranking Quality: The effectiveness depends on the reranking model's ability to identify truly relevant segments.
Context Window Limitations: Very complex reasoning may still exceed the context windows of smaller models.
Domain Specificity: Different domains may require specialized segmentation and evaluation criteria.

Future Directions

Several exciting directions for future research include:

Adaptive Segmentation: Dynamically adjusting segment size based on reasoning complexity
Multi-model Ensembling: Combining reasoning from multiple source models
Self-Evaluation Metrics: Developing better heuristics for evaluating reasoning quality
Domain-Specific Optimizations: Tailoring the approach to specific problem domains

Conclusion

Chain of Thought Reranking represents a significant advancement in improving the reasoning capabilities of large language models. By extracting, segmenting, reranking, and refining the reasoning process, we can enhance the reliability and accuracy of AI systems on complex reasoning tasks.

The approach demonstrates how meta-reasoning—thinking about thinking—can be applied to artificial intelligence systems, bringing us one step closer to more robust and trustworthy AI.

GitHub Repository

For implementation details and code examples, visit our GitHub repository or try the interactive Colab notebook.

Cole McIntosh