Sign In

Detecting Self-Contradictions in Long Documents using Large Language Models

Core Concepts
Large language models struggle to reliably detect self-contradictions in long documents, with GPT4 performing the best among the evaluated models.
The paper introduces CONTRADOC, a new human-annotated dataset for studying self-contradictions in long documents across multiple domains, document lengths, self-contradiction types, and appearance scopes. The dataset is used to evaluate the performance of four state-of-the-art large language models (GPT3.5, GPT4, PaLM2, and LLaMAv2) on three different tasks: binary judgment, top-k evidence selection, and judge-then-find. The key findings are: GPT4 outperforms the other models, but still struggles to reliably detect self-contradictions, especially those that require more nuance and context. Models perform better on detecting self-contradictions related to objective facts (e.g., numeric, negation) compared to more subjective ones (e.g., emotion, perspective). Self-contradictions that are farther apart in the document are not necessarily harder to detect than those that are closer. Document type (news, wiki, story) has a significant impact on model performance, with wiki documents being the easiest and story documents being the hardest. The paper highlights the need for further research to improve large language models' capabilities in long-form reasoning and understanding self-contradictions in text.
"The road T51 was located in New York." "The road T51 was located in California." "The doctor spoke highly of the project and called it 'a breakthrough'." "The doctor disliked the project, saying it had no impact at all." "Zully donated her kidney." "Zully never donated her kidney."
"Detecting contradictions in texts has long been pivotal in natural language understanding(NLU), with most of the works falling under the umbrella of natural language inference(NLI)." "Psychological research (Graesser and McMahen, 1993; Otero and Kintsch, 1992) indicates that humans struggle to identify contradictions in unfamiliar, informative texts, particularly when contradictions are widely separated in long documents, underscoring the need for automated text analysis tools to tackle this challenge."

Deeper Inquiries

How can large language models be further improved to better detect and resolve self-contradictions in long-form text?

Large language models can be enhanced to better detect and resolve self-contradictions in long-form text through several approaches: Fine-tuning on Self-Contradiction Detection: Training language models specifically on self-contradiction detection tasks can improve their ability to identify inconsistencies within text. By providing more annotated data on self-contradictions, the models can learn to recognize patterns and linguistic cues that indicate contradictory statements. Contextual Understanding: Enhancing the models' contextual understanding capabilities can help them grasp the nuanced relationships between different parts of a document. This can involve incorporating more sophisticated attention mechanisms or memory modules to capture long-range dependencies and context. Multi-Modal Inputs: Integrating multiple modalities such as text, images, and graphs can provide additional context for detecting self-contradictions. Models that can effectively combine information from different modalities may have a better grasp of the overall context and potential contradictions. Explainability and Evidence Extraction: Improving the models' ability to provide explanations for their predictions can help in identifying the evidence that supports a self-contradiction. By highlighting the specific parts of the text that lead to a contradictory conclusion, the models can offer more transparent and interpretable results. Domain-Specific Knowledge: Incorporating domain-specific knowledge bases or ontologies can enhance the models' understanding of specific topics and improve their accuracy in detecting self-contradictions within domain-specific documents. Adversarial Training: Training language models with adversarial examples that contain subtle contradictions can help them become more robust in identifying inconsistencies. By exposing the models to a diverse range of contradictory instances, they can learn to generalize better to unseen cases.

How can the insights from this work on self-contradiction detection be applied to other document-level reasoning tasks, such as summarization, question answering, or fact-checking?

The insights gained from the study on self-contradiction detection can be leveraged in various ways to enhance other document-level reasoning tasks: Summarization: By integrating self-contradiction detection capabilities into summarization models, the system can ensure that the generated summaries are coherent and free from internal inconsistencies. This can lead to more accurate and reliable summaries that capture the essence of the original document without contradictions. Question Answering: Incorporating self-contradiction detection mechanisms in question-answering systems can help improve the accuracy and reliability of the answers provided. The models can verify the consistency of information across different parts of the document before generating responses, leading to more trustworthy answers. Fact-Checking: In the realm of fact-checking, detecting self-contradictions can serve as a valuable signal for identifying potentially false or misleading information. By flagging contradictory statements within documents, fact-checking systems can prioritize content for further verification and analysis, aiding in the identification of misinformation. Contextual Understanding: The ability to detect self-contradictions requires a deep understanding of the context and relationships within a document. This contextual understanding can benefit a wide range of document-level reasoning tasks by improving the models' comprehension of complex information and enhancing their reasoning capabilities. Model Robustness: Training models to detect self-contradictions can contribute to their overall robustness and reliability in handling diverse document types and tasks. Models that can identify and resolve inconsistencies are likely to perform better across a range of document-level reasoning challenges, leading to more accurate and trustworthy results.