Ensembling for Text Generation in Large Language Models (LLMs)

Model ensembling is a well-established technique in Natural Language Processing (NLP) that involves combining the predictions of multiple models to achieve improved performance and robustness. This principle extends effectively to Large Language Models (LLMs), particularly for tasks involving text generation.

1. What is Model Ensembling?

Model ensembling refers to the method of aggregating predictions from multiple models to generate a final output that is more accurate or stable than any single model alone. This technique can be applied in various forms:

• Model Ensembling: Using multiple distinct LLMs, each receiving the same prompt, and combining their outputs.
• Prompt Ensembling: Using a single LLM with multiple different formulations of the same task (prompts), and combining the outputs.
• Output Ensembling: Using a single LLM with a single prompt, but sampling multiple outputs from it and selecting the best one or aggregating them.

These techniques are powerful because they allow capturing a broader range of reasoning paths, linguistic expressions, and solution strategies.

2. Prompt Ensembling in LLMs

Prompt ensembling involves prompting a single LLM using different formulations of the same task. For instance, to simplify text, we can use diverse prompts such as:

• "Make this text simpler."
• "Condense and simplify this text."
• "Rewrite for easy reading."

Each prompt may elicit a slightly different response. These varied outputs can then be combined to produce a more effective or balanced final output.

Formal Definition

Let ${x_1, x_2, \dots, x_k}$ be a set of $k$ distinct prompts for a given task. An LLM generates an output for each prompt, denoted by $\hat{y}_i$, using:

$\hat{y}_i = \text{argmax}_y P(y | x_i)$

These individual outputs are then merged using a combination function:

$\hat{y} = \text{Combine}(\hat{y}_1, \hat{y}_2, \dots, \hat{y}_k)$

3. Combination Strategies

Several strategies exist to combine predictions from different prompts or outputs:

• Majority Voting: Select the output that is most frequently agreed upon among the variants.
• Overlap Selection: Choose the output that shares the most tokens or semantic meaning with other generated outputs.
• Token-Level Averaging: At each generation step, average the token probabilities across different prompts and select the token with the highest average probability.

The formula for token-level averaging at the $j$-th token position can be represented as:

$\hat{y}j = \text{argmax}{y_j} \sum_{k=1}^{K} \log P(y_j | x_k, \text{history})$

where $K$ is the number of prompts, and history represents the previously generated tokens.

4. Bayesian View of Prompt Ensembling

From a Bayesian perspective, different prompts can be viewed as representations of latent variables associated with a given problem $p$. The full predictive distribution of the output $y$ given the problem $p$ is computed by marginalizing over all possible prompts $x$:

$P(y | p) = \int P(y | x) P(x | p) dx$

In practice, this integral is often approximated using sampling techniques, such as Monte Carlo methods, due to the vast and continuous space of potential prompts.

5. Generating Diverse Prompts

Prompt diversity is crucial for the effectiveness of ensembling. Some methods for generating diverse prompts include:

• Manual Design: Carefully crafting multiple demonstrations or instructions for the task.
• Automated Generation: Using LLMs themselves to generate variations of prompts.
• Example Shuffling: If using few-shot examples, shuffling their order within the prompt.
• Paraphrasing/Translation: Creating paraphrased or translated versions of existing prompts.
• Hybrid Methods: Combining several of the above approaches.

Note: For highly capable LLMs, minor changes in prompts might lead to minimal output variance. However, prompt ensembling remains valuable for complex or ambiguous tasks where different prompt formulations can uncover different aspects of the model's reasoning.

6. Output Ensembling (Hypothesis Sampling)

Output ensembling, also known as hypothesis sampling, involves generating multiple outputs from a single prompt using a single LLM. Common techniques for this include:

• Beam Search: Exploring multiple potential sequences of tokens simultaneously and returning the top $N$ most probable hypotheses.
• Stochastic Sampling: Using temperature-controlled decoding (e.g., setting a temperature $> 0$) to introduce randomness and generate diverse outputs.

These sampled outputs are then aggregated using strategies like self-consistency.

7. Self-Consistency in Output Ensembling

Self-consistency enhances prediction reliability by identifying the most frequent answer across multiple sampled outputs, particularly for tasks requiring reasoning. This method doesn't alter the model or prompt but varies the outputs through sampling.

Example: For a problem asking for the probability of a coin flip landing heads, if three sampled outputs are:

• Output 1: 37.5%
• Output 2: 37.5%
• Output 3: 33.3%

Self-consistency would select 37.5% as the final answer due to majority agreement.

8. Output Selection Techniques

Several methods can be employed to select the best output from a set of candidates:

• Answer Voting (Self-Consistency): Selecting the answer that appears most frequently.
• Scoring Models (Verifiers): Employing trained models to evaluate the generated outputs. These verifiers can be:
  • Outcome-based: Scoring the entire reasoning chain or final output.
  • Process-based: Scoring each step within a chain of reasoning.

These verifiers can be trained as binary classifiers to label predictions as correct or incorrect. Scoring can be further refined by training verifiers on labeled datasets of generated outputs.

9. Predict-Then-Verify Paradigm

Many advanced prompting techniques, including ensembling strategies, follow a "predict-then-verify" framework:

• Self-Refinement: Initial predictions are generated and then iteratively improved or rewritten.
• Output Ensembling: A pool of answers is generated, and a selection mechanism (e.g., a verifier or voting) picks the best one.
• Verifier Models: Trained models explicitly evaluate the quality or correctness of generated reasoning paths or outputs.

These techniques share similarities with reward model training in Reinforcement Learning from Human Feedback (RLHF), although their specific objectives may differ.

10. Visualization of Ensembling Methods

The three primary types of ensembling can be visualized as follows:

(a) Model Ensembling

• Multiple distinct LLMs receive the same prompt.
• Each LLM generates an independent output.
• The outputs from these different models are then combined.

(b) Prompt Ensembling

• A single LLM is used.
• The LLM is presented with multiple diverse prompts for the same task.
• Outputs generated from these different prompts are then combined.

(c) Output Ensembling

• A single LLM is used with a single prompt.
• Multiple outputs are sampled from this combination.
• The best output is selected or outputs are aggregated.

Conclusion

Model ensembling in LLMs, whether through model, prompt, or output ensembling, is a potent method for enhancing text generation quality. It introduces diversity, robustness, and better alignment with desired outcomes, particularly for complex or ambiguous tasks. These methods form the foundation for advanced techniques in Chain-of-Thought (CoT) prompting, verification, and self-refinement strategies.

By understanding and applying these ensembling techniques, developers and researchers can significantly improve the reliability and performance of LLM-based applications.

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Interview Questions

• What is model ensembling in the context of large language models (LLMs)?
• How does prompt ensembling differ from output ensembling?
• What are some common methods used to combine outputs from different prompts or samples?
• Explain how token-level averaging works in ensemble decoding.
• What is self-consistency in output ensembling, and why is it useful?
• How can Bayesian inference be applied to prompt ensembling in LLMs?
• Describe strategies for generating diverse prompts to improve ensemble results.
• What is the predict-then-verify paradigm, and how does it relate to ensembling?
• How do verifier models help in selecting the best output from multiple candidates?
• What are the trade-offs between beam search and temperature sampling in output generation for ensembling?