Long-form Hallucination Detection with Self-elicitation

Authors: Zihang Liu, Jiawei Guo, Xinxin Lu, Hao Zhang, Jiajun Bu, Haishuai Wang
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: hallucination, knowledge graph, large language models, medical QA

Abstract:
While Large Language Models (LLMs) have exhibited impressive performance in long-form question-answering tasks, they frequently present a hazard of producing factual inaccuracies or hallucinations. An effective strategy to mitigate this hazard is to leverage off-the-shelf LLMs to detect hallucinations after the generation. The primary challenge resides in the comprehensive elicitation of the intrinsic knowledge acquired during their pre-training phase. However, existing methods that employ complex reasoning chains predominantly fall short of addressing this issue. Moreover, since existing methods for hallucination detection tend to decompose the text into isolated statements, they are unable to understand the inherent in-context semantics in long-form content. In this paper, we propose a novel framework, SelfElicit, which synergizes the self-elicitation of intrinsic knowledge of large language models and long-form continuity understanding. Specifically, we leverage self-generated thoughts derived from prior statements as catalysts to elicit the expression of intrinsic knowledge, which is integrated with graph structures to alleviate induced hallucinations and guide the factual evaluation by effectively organizing the elicited knowledge. Extensive experiments on real-world QA datasets demonstrate the effectiveness of self-elicitation and the superiority of our proposed method.

Decision: reject
Reviewer scores: 3, 6, 5, 6

Reviews and discussion:
<thread>
creation date:
1730033853744

thread_type:
official_review

<thread_object>
creator: reviewer_efcv

<content>
summary:
The paper presents an integrated long-form hallucination detection mechanism, SelfElicit, which centers around a knowledge hypergraph and includes a series of introspective steps, allowing hallucination detection without the need for external knowledge retrieval. This approach demonstrates academic originality and effectively expands the applicability of hallucination detection. The paper is well-structured, employs precise language, and demonstrates rigorous logic. Extensive experiments validate the effectiveness of the proposed SelfElicit framework, and quantitative analyses further investigate the quality of insights generated by SelfElicit. I believe this paper will make a meaningful contribution to the field.

strengths:
1.The paper introduces an innovative long-form hallucination detection mechanism, SelfElicit, which synergizes the self-elicitability of inherent knowledge within large language models with long-form continuity comprehension. This method skillfully leverages the concept of self-generation within large models to elicit expressions of their intrinsic knowledge.

2.The paper is well-structured, with a clear description of the methodology and a comprehensive experimental section.

3.This work expands the applicability of hallucination detection through SelfElicit, potentially contributing meaningfully to the research community.

weaknesses:
1. Some viewpoints in the paper lack factual support. For instance, in line 214, the statement "prompting the model to provide reflections on the evaluation" lacks evidence or justification.

2. Although mentioned in the Limitations section, it is worth emphasizing that the experiments are conducted solely on real-world medical QA datasets. This limitation narrows the scope of general conclusions and reduces the credibility of SelfElicit’s generalizability across diverse fields.

3. In the Main Results section, the baseline is locally implemented rather than based on data from previous research, which diminishes the persuasive strength of the experimental findings.

questions:
1. Why is the "prompting the model to provide reflections" approach a more effective method? This actually corresponds precisely to weaknesses 1.

2. When handling statements categorized as "Contradict" the model still relies on its own judgment to identify conflicts and make corrections. Does this approach lead to cumulative errors? In other words, as the framework operates through its complete chain, it continuously encounters potential hallucinations. How do you view the cumulative error introduced by this process?

3. In this workflow, the knowledge hypergraph provides a stable limitation on LLM hallucinations. However, during updates, the hypergraph itself might become contaminated with hallucinated information. Notably, in the w/o conflict setting, the performance decline is substantial. Since the article only briefly addresses this issue in line 257, I would like to understand further how this contamination is mitigated.

4. In the proposed self-elicit method, is the core mechanism based more on the model's intrinsic knowledge expression or the knowledge hypergraph?

5. In the Main Results section, the baseline was implemented locally rather than using data from other studies, which may weaken the persuasive power of the experimental results. Could you provide comparative data from similar scenarios in other literature to strengthen credibility? (For example, using results from SelfCheckGPT under comparable experimental conditions).
</content>
</thread_object>

</thread>
<thread>
creation date:
1730467869579

thread_type:
official_review

<thread_object>
creator: reviewer_gtbe

<content>
summary:
The paper "Long-Form Hallucination Detection with Self-Elicitation" presents a novel approach to address the issue of factual inaccuracies, or hallucinations, in the output of Large Language Models (LLMs), especially in the context of long-form text generation. The authors introduce SelfElicit, a framework that leverages the intrinsic knowledge of LLMs and the semantic continuity within long-form content to effectively detect and mitigate hallucinations. The key contributions include a self-elicitation mechanism to elicit the models' knowledge, the use of a knowledge hypergraph for organized knowledge retention and inconsistency resolution, and the demonstration of the framework's effectiveness through extensive experiments on real-world medical QA datasets. The paper claims that SelfElicit outperforms existing methods in detecting hallucinations in long-form content, offering a significant step towards improving the reliability of LLM-generated text.

strengths:
1. The paper uniquely addresses the semantic continuity in long-form content, which is often overlooked, and shows how this can improve the detection of factual inaccuracies.
2. The use of a knowledge hypergraph for organizing and retaining knowledge is a significant strength, as it helps in managing complexity and resolving inconsistencies in the model's knowledge base.
3. The paper clearly identifies the problem of hallucination in LLMs and provides a well-aligned solution, making it easy for readers to follow the rationale behind the proposed framework and its components.

weaknesses:
1. The paper's focus on medical QA datasets raises concerns about the framework's ability to generalize to other domains. The lack of experimentation in diverse domains means that the robustness and applicability of SelfElicit across different types of long-form content remain unproven.
2. The theoretical underpinnings of the self-elicitation process and its interaction with the knowledge hypergraph are not thoroughly explored. The paper does not provide a deep dive into the mathematical or conceptual models that would support the claims about the framework's effectiveness.
3. The paper heavily relies on quantitative metrics to evaluate the performance of the SelfElicit framework. However, there is an absence of qualitative analysis, such as case studies or detailed error analysis, which could provide a more nuanced understanding of the framework's strengths and weaknesses in practical scenarios.
4. The visual representation in the paper, particularly in Figure 2, is not sufficiently clear or expressive, which can lead to confusion about the SelfElicit framework's methodology. The diagrams fail to effectively communicate the process, detracting from the reader's understanding and the overall quality of the presentation. A redesign of these figures is recommended for better clarity.
5. The paper claims that SelfElicit outperforms existing methods but does not provide a detailed analysis or comparison with SOTA techniques. The superficial nature of these comparisons leaves room for doubt regarding the true novelty and superiority of the proposed framework.

questions:
1. How well does the SelfElicit framework generalize to different domains beyond medical QA? Have there been any preliminary tests or simulations to assess its performance on long-form content from other fields, such as legal or historical texts?
2. What is the theoretical foundation that underpins the self-elicitation mechanism and its interaction with the knowledge hypergraph? Could the authors provide more details on any formal proofs or mathematical models that validate the effectiveness of this approach?
3. What are the scalability considerations of the SelfElicit framework? How does its performance and accuracy change with increasingly larger and more complex datasets, and are there any optimizations in place to handle such scalability challenges?
</content>
</thread_object>

</thread>
<thread>
creation date:
1730497013547

thread_type:
official_review

<thread_object>
creator: reviewer_bjeq

<content>
summary:
The paper presents a novel framework called SelfElicit for detecting hallucinations in long-form content generated by Large Language Models (LLMs). The approach leverages self-elicitation of intrinsic knowledge and understanding of semantic continuity to improve hallucination detection.

strengths:
Improved Hallucination Detection: The framework effectively detects hallucinations by leveraging self-elicitation and semantic continuity, resulting in superior performance compared to existing methods.

Intrinsic Knowledge Utilization: By eliciting intrinsic knowledge from LLMs, the approach enhances the factuality and diversity of knowledge expression.

Graph-Based Contextual Understanding: The use of a knowledge hypergraph allows for better organization and understanding of semantic continuity, aiding in more accurate evaluations.

Iterative Improvement: The framework iteratively refines its understanding and knowledge base, potentially leading to more accurate and reliable results over time.

weaknesses:
Complexity: The approach involves multiple steps and components, which may increase the complexity of implementation and computational overhead. To better understand the scalability of the framework, could the authors provide a detailed analysis of the computational complexity? Additionally, it would be helpful to see comparisons with existing methods in terms of implementation complexity and runtime. Not so sure about scalability here. 

Dependency on LLMs: The performance of the framework is inherently tied to the capabilities of the underlying LLMs, which may limit its effectiveness in certain scenarios. Could the authors provide a more detailed analysis of how the framework's performance varies with different LLMs? It would also be beneficial to include a comparison with non-LLM based approaches and a cost-benefit analysis that weighs the improved performance against potential increased costs.

Potential for Induced Hallucinations: While the framework aims to mitigate hallucinations, the process of self-elicitation and reflection may still introduce inaccuracies or hallucinations, especially with ambiguous or unfamiliar content. Have the authors considered implementing additional safeguards or validation steps to catch induced hallucinations? Specific strategies or examples would be helpful in understanding how this limitation might be addressed.

questions:
How does the framework handle cases where the LLM lacks sufficient intrinsic knowledge about a specific topic?

What are the computational costs associated with maintaining and updating the knowledge hypergraph, especially for large datasets? Could the authors provide specific metrics or comparisons, such as how these costs scale with the size of the input or the number of iterations?

How does the framework handle cases where the LLM lacks sufficient intrinsic knowledge about a specific topic? Have the authors tested the framework's performance on topics known to be outside the LLM's training data? Additionally, is there a fallback mechanism in place for when the LLM expresses low confidence?

Can the approach be adapted or extended to work with external knowledge sources to further enhance hallucination detection?

How does the framework perform across different domains and languages, and what adaptations might be necessary for domain-specific applications?
</content>
</thread_object>

</thread>
<thread>
creation date:
1730693864768

thread_type:
official_review

<thread_object>
creator: reviewer_lcez

<content>
summary:
This paper introduces SelfElicit, a novel framework for detecting hallucinations in long-form content generated by Large Language Models (LLMs). The key innovation is using self-generated thoughts from prior statements to elicit a model's intrinsic knowledge, combined with a knowledge hypergraph to organize and validate the elicited information.

strengths:
Pros:
1. The paper addresses a significant and timely problem in LLM research - hallucination detection in long-form content. 
2. The authors demonstrate through careful ablation studies that their approach outperforms variants without self-elicitation by 3.0% on average. The elicitation quality analysis (Figure 3) provides compelling evidence that self-generated thoughts lead to both higher factuality and diversity compared to alternative approaches.
3. The authors show through their ablation studies that removing any of these components leads to performance degradation, with the full method outperforming ablated variants by 2.1-6.5% on average.

weaknesses:
Cons:
1. There is no related work section. It is strongly suggested that the authors conduct a comprehensive literature review, especially on hallucination detection. Otherwise, this paper is actually not complete.
2. "For the convenience in writing, we slightly abuse the term “non-factual” to represent both factuality and faithfulness hallucinations (Huang et al., 2023). Yet our work is not limited to either one" is confusing. What type of hallucination does this paper focus on? It is suggested that the authors explicitly state the type of hallucinations rather than mix them.
3. "they are unable to learn the inherent semantic continuity in long-form content." (Line 19) is unclear. It is suggested that the authors define "semantic continuity" before they use it, even in the abstract.
4. "However, there remains a concern regarding their tendency to generate hallucinations (Bang et al., 2023), producing sentences with plausible looking yet factually1 unsupported content (Huang et al., 2023) and hurting their faithfulness in real-world scenarios expecting factually-accurate response (Wei et al., 2024)." How do the authors define "faithfulness"? It is suggested that the authors differentiate "faithfulness" and "factuality". Otherwise, it is confusing.
5. The example “Gliclazide can be taken at any time of the day→, regardless of whether it is on an empty stomach or after meals→” needs more explanations why it is misleading.
6. "While insightful, we contend that these methods either require complex manual prompts or involve intricate reasoning processes, which limit their elicitation capacity and increase the risk of accumulated inaccuracies and hallucinations." (Line 67) needs more explanations
7. The evaluated LLMs are relatively limited and small-scale. It is suggested that the authors also evaluate on SOTA models such as GPT-4 and models with a larger scale such as llama-70B. Otherwise, the effectiveness of the proposed method is relatively limited.
8. The authors only evaluate the performance of the proposed methods on their own dataset. Could the authors also test the effectiveness on public datasets?
9. The datasets are not open-sourced. The construction process of the dataset is also not clear.
10. "knowledge hypergraph" (line 87) is unclear. It is suggested that the authors define "knowledge hypergraph" before they use it.
11. The paper's evaluation is limited to the medical domain. The lack of evaluation across other domains makes it difficult to assess the general applicability of the approach.
12. The conflict resolution mechanism, while important, relies heavily on Natural Language Inference (NLI). The paper doesn't provide detailed analysis of how different NLI approaches might impact the overall performance, nor does it discuss potential failure modes of the NLI component. This is a crucial component that deserves more thorough investigation.

# Comment after Rebuttal

Thanks for the rebuttal. I decide to maintain my score and suggest another round of revision. Thanks.

questions:
See weaknesses
</content>
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</thread>
<thread>
creation date:
1732129355581

thread_type:
official_comment

<thread_object>
creator: authors

<content>
title:
Response to AC and all reviewers

comment:
We sincerely thank the reviewers for thoroughly examining this manuscript and providing valuable insights for the further improvements of this work.

We have carefully considered these important suggestions and revised them accordingly. Please find our detailed responses to the reviewers' comments below. Additionally, we have made the following improvements to the paper (important modifications are highlighted in the main text in our revised version). Some important modifications are listed as follows:

+ Additional experiments on **a public dataset about biography**, namely WikiBio, are included. The results show that our method generates well across domains.
+ Additional experiments with **state-of-the-art modern LLMs** including Qwen2.5, Llama3.1, and gpt4o-mini is included.  The results show that our method has superior performance across LLM backbones.
+ Re-categorized the baselines to highlight their characteristics with various elicitation approaches.
+ **Add two context-argument baselines** (ContextIO and HistoryIO) for a more intuitive comparison. We have also modified Table 1 to show more metrics.
+ Two **case studies** for qualitative analysis are included.
+ Clarify the main focus of this paper is the factual (external) hallucination and carefully differentiate the terminologies.
+ Redesign Figure 2 for a better understanding of the overall framework.
+ The Appendix is included at the end of the main text rather than a separate file with modifications.
</content>
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</thread>


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