Determine-Then-Ensemble: Necessity of Top-k Union for Large Language Model Ensembling

Authors: Yuxuan Yao, Han Wu, Mingyang LIU, Sichun Luo, Xiongwei Han, Jie Liu, Zhijiang Guo, Linqi Song
Primary area: other topics in machine learning (i.e., none of the above)
Keywords: Model ensembling, LLM

Abstract:
Large language models (LLMs) exhibit varying strengths and weaknesses across different tasks, prompting recent studies to explore the benefits of ensembling models to leverage their complementary advantages. However, existing LLM ensembling methods often overlook model compatibility and struggle with inefficient alignment of probabilities across the entire vocabulary. In this study, we empirically investigate the factors influencing ensemble performance, identifying model performance, vocabulary size, and response style as key determinants, revealing that compatibility among models is essential for effective ensembling. This analysis leads to the development of a simple yet effective model selection strategy that identifies compatible models. Additionally, we introduce the \textsc{Uni}on \textsc{T}op-$k$ \textsc{E}nsembling (\textsc{UniTE}), a novel approach that efficiently combines models by focusing on the union of the top-k tokens from each model, thereby avoiding the need for full vocabulary alignment and reducing computational overhead. Extensive evaluations across multiple benchmarks demonstrate that \textsc{UniTE} significantly enhances performance compared to existing methods, offering a more efficient framework for LLM ensembling.

Decision: accept (spotlight)
Reviewer scores: 6, 8, 8, 8

Reviews and discussion:
<thread>
creation date:
1730583181843

thread_type:
official_review

<thread_object>
creator: reviewer_22sv

<content>
summary:
Paper has two main contributions:

1. Presents a thorough analysis of features affecting ensemble performance: bade model performance gap, vocabulary size, and response style consistency.
2. A new ensembling method, UNITE, that focuses on combining only the top-k tokens instead of the entire vocabulary. The method is efficient in runtime and shows a high performance across multiple benchmarks.

strengths:
1. UNITE’s token-level aggregation without full vocabulary alignment is an innovative method which reduced computational needs and making ensembling more efficient.
2. Empirical results are shown over a concrete set of benchmarks.
3. Their focus on model selection strategy brings up insightful practical guidelines which are very useful in practice.
4. They present clear results comparing latency against existing methods.

weaknesses:
1. Lack of analysis on possible limitations or settings or benchmarks where topk-k token alignment fails to improve base models perf.
2. Figures and tables could have more detailed captions with information to be self-contained. (ex figures 4,5 and 6)
3. Figures and plots' font size are very small. Consider increasing the font size to assists readers.

questions:
1. Do you have an insight about why increasing k beyond 10 does not improve perf? I think this result is counter-intuitive, and needs more analysis.
2. What is the impact of base model perf difference when using UNITE?
</content>
</thread_object>

</thread>
<thread>
creation date:
1730705299022

thread_type:
official_review

<thread_object>
creator: reviewer_qxta

<content>
summary:
This paper proposed an LLM ensemble method, which is to ensemble among only top-k tokens probability.

strengths:
1. It includes good analysis and preliminary experiments for LLM ensembling.
2. Based on the preliminary analysis, they reduce the vocabulary for the previous method into the top-k selection for the model ensembling.

weaknesses:
1. I believe the experiments should include the efficiency compared to the previous approach.
2. It seems, in theory, that including the whole vocabulary should work better, although maybe marginally or at least the same compared to top-k, since it includes the whole picture of the token distribution. I am questioning your experimental results because top-k is better in every dataset, which logically cannot be the case.

questions:
Like Weaknesses#2 above, can you provide reasons or analysis why the top-k approach is better in performance than the whole vocabulary on performance?
</content>
</thread_object>

</thread>
<thread>
creation date:
1730720653097

thread_type:
official_review

<thread_object>
creator: reviewer_qmez

<content>
summary:
The paper identifies and tests three hypotheses about importance factors that influence the performance of logit-based LLM ensembles: performance discrepancy, vocabulary size differences, and stylistic response differences, and provides guidelines for choosing models to ensemble with. The authors propose UNITE, an ensembling method that uses the top-k logits from each model, and show that it outperforms numerous ensembling baselines while being significantly cheaper.

strengths:
- The paper presents concrete empirical conclusions about what factors are actually important for ensembling (performance gap, response style) or not (vocabulary size difference.)
- It presents UNITE, a novel top-k union ensembling approach that drops the computational overhead tremendously by using the top-k tokens of each model instead of the full vocabulary, and that obviates the need for full vocabulary alignment.
- UNITE significantly outperforms the baselines (LLM-Blender, DeePen, GAC) in average accuracy across six popular benchmarks for question-answering, reasoning, and knowledge.  
- The paper is clear and well written.

weaknesses:
- The analysis in Conclusion II on vocabulary size differences is restricted to English language tasks. It may benefit from a discussion of multilinguality, where tokenization might be less consistent between models.
- In Conclusion III the paper identifies differences in response style as a major problem for ensembling models. The proposed solution of limiting longer responses to 2x the length of shorter ones would benefit from theoretical justification. A robust solution to this problem which enables ensembling of models with different response styles would be ideal, as the current approach limits the practicality of the method.

questions:
Does the optimal value of k for the top-k tokens vary across tasks and domains? Is there a principled way to determine k?
</content>
</thread_object>

</thread>
<thread>
creation date:
1730802166002

thread_type:
official_review

<thread_object>
creator: reviewer_nt9f

<content>
summary:
This paper introduces a novel ensembling approach, UNITE (Union Top-k Ensembling), that efficiently integrates large language models (LLMs) by focusing on the union of top-k tokens from each model rather than aligning the full vocabulary. It seeks to improve the computational efficiency and effectiveness of LLM ensembles by addressing key issues of compatibility, vocabulary size, and response styles. The authors propose a model selection strategy to identify compatible models, limiting the influence of incompatible LLMs on the ensemble's performance. Experimental results across multiple benchmarks validate the benefits of UNITE in enhancing performance, reducing latency, and decreasing the computational burden.

strengths:
- The paper identifies compatibility challenges in LLM ensembling and focuses on top-k tokens, aligning this strategy with empirical evidence that vocabulary alignment often introduces computational inefficiencies. 
- The authors conduct extensive experiments on multiple models and benchmarks, analyzing factors like vocabulary size, response style, and latency. The results support UNITE’s superiority in maintaining high performance while minimizing latency and token manipulation.
-  The proposed determine-then-ensemble strategy offers a generalizable framework for selecting compatible models, making the findings applicable to real-world LLM applications that require efficient model collaboration.

weaknesses:
- While the paper addresses task-specific challenges, it could benefit from deeper exploration into why certain tasks (like MMLU) see greater performance improvements than others. Further insight into how task characteristics impact ensembling effectiveness would add depth to the analysis.
- The model selection process relies on response style consistency and performance alignment within a 10% range, which may limit scalability when dealing with a large pool of candidate models. The method would benefit from a more automated or quantitative metric for determining compatibility.
- While UNITE is evaluated across standard datasets, some benchmarks like GSM8K and TriviaQA may not fully capture the diverse range of LLM applications. Including more varied tasks could strengthen the argument for UNITE’s general applicability.

questions:
- Could you provide more theoretical support or intuition for why limiting alignment to the top-k tokens effectively enhances ensemble performance? How does this approach balance between accuracy and computational efficiency at a probabilistic level?
- How does UNITE handle models with markedly different response styles in practice? Would introducing a preprocessing step to standardize response formats (e.g., for tasks like QA or summarization) enhance compatibility?
- Since UNITE is partially motivated by efficiency, adding a comparative breakdown of memory and latency for each method would clarify the computational trade-offs involved. Including charts or tables that detail average and worst-case latency per token would help underscore UNITE’s operational benefits.
</content>
</thread_object>

</thread>


Metareview:
metareview:
Many LLMs have been released recently, each of which is trained on different data and has its strengths and weaknesses when performing downstream tasks. This paper proposes a new method for ensembling LLMs, UNITE (UNIon Top-k Ensembling), to achieve better performance than any individual language model in the ensemble. UNITE ensembles LLMs by taking the union of the top-k tokens predicted by each model rather than considering the full vocabulary of the models; thus, the approach is more efficient than prior LLM ensembling methods and handles the compatibility challenges that come with ensembling diverse LLMs with different vocabularies. Furthermore, the experiments show that UNITE outperforms these prior approaches on multiple downstream tasks, despite the reduced computational overhead of the method.

Strengths:
- UNITE is an effective, general method for ensembling LLMs that improves over prior methods in this space while also solving an existing issue (compatibility across vocabularies)and reducing the computational costs of ensembling (nt9F, qMEZ, 22sv).
- The paper also extensively analyzes the factors that make ensembing LLMs effective (nt9F, qMEZ, QXTA).
- During revisions, the authors incorporated multiple experiments to address the reviewers' concerns, including a new LLM benchmark and an additional evaluation in a new language (Chinese) with CMMLU.

Weaknesses:
While the paper contains evaluations on many different benchmarks, there is limited analysis as to why some benchmarks benefit more from the UNITE method than others (nt9F) or in cases when the method fails (22sv).

Most of the other weaknesses raised by the reviewers, such as the effect of response style, were fully addressed in the author response. I recommend that the authors increase the size of the font in the figures, as they are currently difficult to read.