Integrative Decoding: Improving Factuality via Implicit Self-consistency

Authors: Yi Cheng, Xiao Liang, Yeyun Gong, Wen Xiao, Song Wang, Yuji Zhang, Wenjun Hou, Kaishuai Xu, Wenge Liu, Wenjie Li, Jian Jiao, Qi Chen, Peng CHENG, Wayne Xiong
Primary area: generative models
Keywords: Large Language Models, Hallucination, Factuality, Decoding

Abstract:
Self-consistency-based approaches, which involve repeatedly sampling multiple outputs and selecting the most consistent one as the final response, prove to be remarkably effective in improving the factual accuracy of large language models. Nonetheless, existing methods usually have strict constraints on the task format, largely limiting their applicability. In this paper, we present Integrative Decoding (ID), to unlock the potential of self-consistency in open-ended generation tasks. ID operates by constructing a set of inputs, each prepended with a previously sampled response, and then processes them concurrently, with the next token being selected by aggregating of all their corresponding predictions at each decoding step. In essence, this simple approach implicitly incorporates self-consistency in the decoding objective. Extensive evaluation shows that ID consistently enhances factuality over a wide range of language models, with substantial improvements on the TruthfulQA (+11.2%), Biographies (+15.4%) and LongFact (+8.5%) benchmarks. The performance gains amplify progressively as the number of sampled responses increases, indicating the potential of ID to scale up with repeated sampling.

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

Reviews and discussion:
<thread>
creation date:
1729341067626

thread_type:
official_review

<thread_object>
creator: reviewer_q7yl

<content>
summary:
This paper presents Integrative Decoding (ID), a new decoding strategy for improving the factual accuracy of LLM by incorporating self-consistency during decoding. ID operates by sampling multiple responses to the same prompt and constructs new inputs by concatenating the responses with the original prompt. Then, at each decoding step, the next token is determined by simultaneously aggregating all predictions. It shows significant performance improvements across different LLMs on open-ended tasks, such as TruthfulQA, Biographies, and LongFact.

strengths:
1. The paper is easy to follow.
2. The method proposed in this paper doesn't need additional prompting or training, making it more applicable to a wider range of scenarios than previous works.
3. The experimental results show that the performance of this method consistently improves with an increase in the number of sampled responses. In contrast, other methods do not, demonstrating the stability of this approach.

weaknesses:
1. Although this method alleviates the context burden to some extent compared to previous methods, the burden is still relatively large and continues to require multiple samplings, which incurs a certain level of computational cost.
2. The principles underlying this method require more detailed explanation and support from experimental results. Especially the reasonableness of the assumption.
3. In the "3.5 CASE STUDY" section, examples corresponding to the SR and USC methods are not provided.
4. Although this paper mentions that ID can maintain coherence, it lacks quantitative evaluation results regarding the coherence and fluency of the responses generated by this method.

questions:
1. Could you provide a more detailed explanation of why the "Context Length" of the ID method is "×2"?
</content>
</thread_object>

</thread>
<thread>
creation date:
1729513356876

thread_type:
official_review

<thread_object>
creator: reviewer_kiks

<content>
summary:
This work proposes integrative decoding (ID), an extension of leveraging self-consistency to enhance the factuality of LLM generations. Specifically, the authors first sample N responses and subsequently use each as a prefix for prompting the LLM to answer the same question again. Finally, a better response is created by ensembling the generations at token-level. Experimental results on three benchmarks validate the effectiveness of the proposed method.

strengths:
1. The writing is clear, and the proposed method is simple and effective.
2. Rather than selecting a response from a set of candidates, ID integrates them through generation, which is interesting.

weaknesses:
1. The main limitation is the lack of comparison with atomic self-consistency approaches [1][2][3]. The study only compares its method with some older baselines (e.g., USC and SR), which have been significantly surpassed by more recent works.
2. The related work section could benefit from discussing the use of self-consistency for uncertainty estimation (e.g., [4]). Self-consistency is not only an approach to improve factuality but is also considered a better estimator of LLM confidence. Incorporating these discussions could potentially deepen the motivation behind this research direction.


[1] Atomic Self-Consistency for Better Long Form Generations.
[2] Integrate the Essence and Eliminate the Dross: Fine-Grained Self-Consistency for Free-Form Language Generation.
[3] Improving LLM Generations via Fine-Grained Self-Endorsement.
[4] LUQ: Long-text Uncertainty Quantification for LLMs

questions:
Please see the above-mentioned weakness.
</content>
</thread_object>

</thread>
<thread>
creation date:
1730605332430

thread_type:
official_review

<thread_object>
creator: reviewer_tzmw

<content>
summary:
In this work, the authors propose a new decoding method called "Integrative Decoding" so that self-consistency can be obtained. The experiments shows that this decoding method can improve factuality. The improvements are shown in three benchmarks (TruthfulQA, Biographies, and LongFact).

strengths:
1. A new decoding method is proposed to improve consistency. Higher factuality are obtained in the experiments.

2. The evaluations are done over six series of LLM with varying scales and multiple benchmarks (TruthfulQA, Biographies, and LongFact).

3. In the analysis, as the number of sampled responses increases, the performance is improved.

weaknesses:
1. It is reasonable that consistency is obtained with this decoding method. However, factuality improvement seems unclear. Improvements seems more possible If the model is well calibrated. Some discussions can be done in this perspective. 

2. In the evaluation of these experiments, LLM (GPT-4) is used for factuality evaluation. However, there is no human annotation results. Miss match could happen for the evaluation. It could cause the actual improvements.

3. The cost of the proposed decoding method is high: double context length is need, and more inference steps (the same as the number of sampled responses). For long-form generation, it is hard and inefficient.

questions:
According to Figure 3, the claim "The performance of integrative decoding progressively improves with more sampled responses across six LLMs." seems wrong. For the three models (Llama3, Gemma2, and GLM4), accuracy is increasing, then decreasing. Could you have a check?
</content>
</thread_object>

</thread>
<thread>
creation date:
1730663361752

thread_type:
official_review

<thread_object>
creator: reviewer_jqqz

<content>
summary:
The "Self-consistency" approach consists in  sampling from a LLM  a set of many different outputs  to select the final hypothesis as the most consistent one with respect to this set. This kind of approach  really helps to improve the "factuality"  of the output. 
This paper builds on this line of research to Integrative Decoding (ID). It allows the use of self consistency approach text generation tasks. The idea :-) is to extend the decoding objective with a consistency term. This new objective can be estimated with a smart and integrative prompt. Experiments show nice  improvements on many benchmarks like TruthfulQA, Biographies and LongFact.

strengths:
This idea is simple and effective. The theoretical formulation is clear, while in practice the solution is relatively straightforward.  The experimental results clearly support the claims.

weaknesses:
The extra cost of this approach is maybe not worst than the other self-consistency based methods. However this drawback could be discussed in the paper. It could be nice to provide inference time, even if it is not the claim of the paper.

questions:
In equation 4, there are two terms f and G. Then their sum is approximated by the inference with the new prompt. Did you try to recover these two terms given this approximation, to see if your motivation sounds ?
</content>
</thread_object>

</thread>
<thread>
creation date:
1732546832186

thread_type:
official_comment

<thread_object>
creator: authors

<content>
title:
Summary of the Rebuttal Period

comment:
Thank you to all the reviewers for their constructive feedback on our work. Below, we provide a brief overview of: (1) the contributions and strengths of our work as highlighted by the reviewers, and (2) the updates made to the manuscript in response to the reviewers' comments.

## **Summary of Strengths and Contributions**

We propose Integrative Decoding (ID), a decoding algorithm that incorporates "self-consistency" within its decoding objective to enhance the factuality of open-ended generation tasks.

- Our approach is **well-grounded in a clear theoretical framework** while being **simple to implement in practice** (Reviewers JqQZ and kiKS).
- It doesn't need additional prompting or training, making it more **applicable to a wider range of scenarios** than previous works (Reviewer Q7YL).
- We conduct **extensive experiments** over six series of LLM with varying scales and multiple benchmarks (Reviewer TZMW). Our approach **consistently demonstrates significant and stable improvements** (Reviewer Q7YL).


## **Issues Addressed in the Rebuttal Period**

In response to the reviewers' suggestions and concerns regarding our work, we have updated the manuscript mainly in the following aspects:

- **Additional Baselines** (Reviewer kiKS): We further compare ID with three more recently proposed techniques, as shown in ``Table 2``.
- **Additional Experiments to Support the Decoding Objective** (Reviewers JqQZ and Q7YL): We add experiments to support that ID can maintain **language coherence** and effectively foster **self-consistency**, as discussed in ``Section 3.6``.
- **Inference Efficiency** (Reviewers JqQZ, TZMW, and Q7YL): We include an evaluation of inference efficiency in ``Section 3.4``. The results indicate that ID achieves **competitive inference efficiency compared to other techniques in this line of research**, while yielding more significant and stable improvements.  We further discuss the value of exploring techniques to utilize more inference-time computation in exchange of enhanced performance in ``Appendix D.1``.
- **Discussion on the Reliability of GPT-4 Evaluation** and **Human Evaluation** (Reviewer TZMW): We mainly use GPT-4-turbo to asssess factuality, providing it with necessary reference information to guide its evaluation (e.g. reference answers, typical wrong answers, and information extracted from wikipedia). We further discuss the reliability of this evaluation approach in ``Appendix D.2`` and add a set of human evaluation in ``Appendix C.1``.

Thank you once again to all the reviewers for their feedback, which has helped us enhance our work to make it more solid and comprehensive.
</content>
</thread_object>

</thread>


Metareview:
metareview:
This paper presents a method for utilizing self consistency via integrative decoding and presents good results on improvements in factuality across several benchmarks.  The paper presents many experiments with comparisons with several baselines.  Overall reviews are positive and I suggest accepting the paper.