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Large Language Models Can Learn Semantic Property Inference Through Experimental Contexts, But Remain Inconsistent and Reliant on Heuristics

Q: How can we develop evaluation methods that can more effectively distinguish between true semantic understanding and reliance on shallow heuristics in LLMs?

This study highlights the crucial need for more sophisticated evaluation methods in LLM research. Here are some strategies to better differentiate between genuine semantic understanding and reliance on shallow heuristics: Adversarial Datasets: Develop datasets specifically designed to trigger known heuristic biases. For example, in the context of property inheritance, create examples where the position of the novel concept is deliberately manipulated to mislead models relying on positional heuristics. Counterfactual Reasoning: Instead of directly asking about a property, pose counterfactual questions that force the model to reason about the implications of a property being different. For instance, "If a wug were actually a type of gorilla, would it still have a flat tail?" Concept-Property Dissociation: Design tests where the connection between concepts and properties is less direct. This could involve using more abstract properties, introducing relationships between multiple properties, or requiring the model to infer implicit properties. Probing Tasks: Develop probing tasks that specifically target the model's internal representations of semantic properties and relationships. This can help determine if the model has learned meaningful representations or is simply memorizing surface-level patterns. Explainable AI (XAI) Techniques: Employ XAI techniques, such as attention visualization or saliency maps, to gain insights into which parts of the input are most influential in the model's decision-making process. This can help identify potential reliance on spurious correlations. By incorporating these strategies, we can create more rigorous evaluations that provide a clearer picture of LLMs' true semantic capabilities.

Q: Could the limitations identified in this study be addressed by incorporating external knowledge bases or symbolic reasoning components into LLM architectures?

The limitations exposed in this study, particularly the reliance on shallow heuristics and the struggle with robust property inheritance, suggest that relying solely on data-driven learning from text might be insufficient. Integrating external knowledge bases and symbolic reasoning components into LLM architectures could offer potential solutions: Knowledge Base Integration: Enhancing Context: Retrieve relevant information from knowledge bases (e.g., ConceptNet, Wikidata) based on the input and provide it as additional context to the LLM. This can supply explicit knowledge about properties, relationships, and common-sense facts. Grounding Concepts: Ground the novel concepts introduced in the prompts to entities in the knowledge base. This can help the model access a richer representation of the concept and its associated properties. Symbolic Reasoning Components: Rule-Based Systems: Incorporate rule-based systems that can perform logical inferences based on the knowledge extracted from the text and the knowledge base. This can enable more systematic and transparent reasoning about property inheritance. Neuro-Symbolic Architectures: Explore hybrid neuro-symbolic architectures that combine the strengths of neural networks (pattern recognition, learning from data) with symbolic reasoning (logical inference, knowledge representation). By combining the inductive biases of LLMs with the explicit knowledge and reasoning capabilities of external systems, we can potentially develop more robust and reliable models for tasks requiring commonsense reasoning.

Q: What are the implications of these findings for the development of LLMs for real-world applications that require robust commonsense reasoning and semantic understanding, such as question answering, dialogue systems, and text summarization?

The study's findings have significant implications for deploying LLMs in real-world scenarios demanding robust commonsense reasoning and semantic understanding: Question Answering: LLMs might provide inaccurate or nonsensical answers if they rely on shallow heuristics instead of true understanding. For instance, in medical diagnosis, relying on superficial patterns could lead to misdiagnosis. Dialogue Systems: Chatbots or virtual assistants could exhibit inconsistent or illogical behavior in conversations, especially when dealing with novel situations or concepts. This could lead to frustrating user experiences and erode trust in the system. Text Summarization: Summaries generated by LLMs might miss crucial information or introduce factual errors if the model fails to grasp the underlying semantic relationships between entities and events in the text. To mitigate these risks, developers should: Prioritize Robustness: Focus on developing LLMs that are less susceptible to shallow heuristics and exhibit more reliable semantic understanding. This might involve exploring alternative training objectives, incorporating knowledge bases, or integrating symbolic reasoning components. Thorough Evaluation: Rigorously evaluate LLMs on tasks and datasets specifically designed to assess commonsense reasoning and semantic understanding. This includes testing for robustness to adversarial examples and unexpected inputs. Human-in-the-Loop Systems: Consider deploying LLMs in human-in-the-loop systems where human experts can monitor the model's outputs, provide feedback, and correct errors. This can help ensure accuracy and reliability in critical applications. By acknowledging these implications and adopting appropriate development and deployment strategies, we can work towards LLMs that are more trustworthy and effective in real-world settings.

Kernekoncepter

While experimental contexts like in-context examples and instructions can improve the ability of large language models (LLMs) to perform semantic property inheritance, this ability remains inconsistent and prone to relying on shallow heuristics, particularly when the task format creates a direct link between the output and positional cues.

Resumé

Bibliographic Information: Misra, K., Ettinger, A., & Mahowald, K. (2024). Experimental Contexts Can Facilitate Robust Semantic Property Inference in Language Models, but Inconsistently. arXiv preprint arXiv:2401.06640v2.
Research Objective: This paper investigates whether experimental contexts, such as in-context examples and instructions, can improve the ability of large language models (LLMs) to perform semantic property inheritance, a task they have previously struggled with.
Methodology: The researchers used the COMPS dataset, which tests property inheritance with minimal pair sentences. They evaluated 12 LLMs of varying sizes, both with and without instruction tuning. They designed experiments with different types of in-context examples and instructions, controlling for positional heuristics that LLMs could exploit. They also reformulated the task into a question-answering format (COMPS-QA) to analyze the impact of task formulation on heuristic reliance.
Key Findings: The study found that experimental contexts can lead to improvements in property inheritance performance in LLMs. However, this improvement was inconsistent, and many LLMs, particularly in the COMPS-QA format, relied on shallow positional heuristics instead of demonstrating robust semantic understanding. Instruction-tuned models showed greater robustness but were not entirely immune to heuristic bias.
Main Conclusions: While experimental contexts can facilitate property inheritance in LLMs, these models still struggle with robust semantic reasoning and often fall back on superficial cues. This highlights the need for further research into developing LLMs that can consistently demonstrate deeper semantic understanding and generalization abilities.
Significance: This research contributes to the ongoing debate about the true reasoning capabilities of LLMs. It provides evidence that while LLMs can learn from experimental contexts, they may not be genuinely understanding and applying semantic knowledge in a human-like way.
Limitations and Future Research: The study was limited to a single dataset (COMPS) and the English language. Future research could explore the generalizability of these findings to other datasets, languages, and semantic reasoning tasks. Additionally, further investigation into the specific mechanisms by which instruction tuning influences heuristic reliance would be beneficial.

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arxiv.org

Statistik

The study evaluated 12 LLMs ranging from 1.5–13B parameters.
The researchers used 10 different in-context learning example sets, each containing 6 different COMPS stimuli.
For their test set, they used a constant set of 256 unique pairs sampled from a pool of stimuli.

Citater

"Conclusions that LMs lack a particular ability may be overhasty if it turns out the ability is easily accessed through in-context learning, different question formulations, or particular instructions."
"Our results suggest that LMs are more likely to show behavior that is compatible with the use of positional heuristics when their output space (choice between the two novel concepts) has a clear connection with positional artifacts in their input (relative ordering of the novel concepts)."
"This suggests that while instruction tuning leads to consistently above-chance performance on challenging property inheritance problems, it is not entirely robust to position-based heuristics."

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Experimental Contexts Can Facilitate Robust Semantic Property Inference in Language Models, but Inconsistently

by Kanishka Mis... kl. arxiv.org 10-18-2024

https://arxiv.org/pdf/2401.06640.pdf

Experimental Contexts Can Facilitate Robust Semantic Property Inference in Language Models, but Inconsistently

Dybere Forespørgsler

How can we develop evaluation methods that can more effectively distinguish between true semantic understanding and reliance on shallow heuristics in LLMs?

This study highlights the crucial need for more sophisticated evaluation methods in LLM research. Here are some strategies to better differentiate between genuine semantic understanding and reliance on shallow heuristics:

Adversarial Datasets: Develop datasets specifically designed to trigger known heuristic biases. For example, in the context of property inheritance, create examples where the position of the novel concept is deliberately manipulated to mislead models relying on positional heuristics.
Counterfactual Reasoning:  Instead of directly asking about a property, pose counterfactual questions that force the model to reason about the implications of a property being different. For instance, "If a wug were actually a type of gorilla, would it still have a flat tail?"
Concept-Property Dissociation: Design tests where the connection between concepts and properties is less direct. This could involve using more abstract properties, introducing relationships between multiple properties, or requiring the model to infer implicit properties.
Probing Tasks: Develop probing tasks that specifically target the model's internal representations of semantic properties and relationships. This can help determine if the model has learned meaningful representations or is simply memorizing surface-level patterns.
Explainable AI (XAI) Techniques: Employ XAI techniques, such as attention visualization or saliency maps, to gain insights into which parts of the input are most influential in the model's decision-making process. This can help identify potential reliance on spurious correlations.
By incorporating these strategies, we can create more rigorous evaluations that provide a clearer picture of LLMs' true semantic capabilities.

Could the limitations identified in this study be addressed by incorporating external knowledge bases or symbolic reasoning components into LLM architectures?

The limitations exposed in this study, particularly the reliance on shallow heuristics and the struggle with robust property inheritance, suggest that relying solely on data-driven learning from text might be insufficient. Integrating external knowledge bases and symbolic reasoning components into LLM architectures could offer potential solutions:

Knowledge Base Integration:

Enhancing Context:  Retrieve relevant information from knowledge bases (e.g., ConceptNet, Wikidata) based on the input and provide it as additional context to the LLM. This can supply explicit knowledge about properties, relationships, and common-sense facts.
Grounding Concepts: Ground the novel concepts introduced in the prompts to entities in the knowledge base. This can help the model access a richer representation of the concept and its associated properties.


Symbolic Reasoning Components:

Rule-Based Systems: Incorporate rule-based systems that can perform logical inferences based on the knowledge extracted from the text and the knowledge base. This can enable more systematic and transparent reasoning about property inheritance.
Neuro-Symbolic Architectures: Explore hybrid neuro-symbolic architectures that combine the strengths of neural networks (pattern recognition, learning from data) with symbolic reasoning (logical inference, knowledge representation).
By combining the inductive biases of LLMs with the explicit knowledge and reasoning capabilities of external systems, we can potentially develop more robust and reliable models for tasks requiring commonsense reasoning.

What are the implications of these findings for the development of LLMs for real-world applications that require robust commonsense reasoning and semantic understanding, such as question answering, dialogue systems, and text summarization?

The study's findings have significant implications for deploying LLMs in real-world scenarios demanding robust commonsense reasoning and semantic understanding:

Question Answering: LLMs might provide inaccurate or nonsensical answers if they rely on shallow heuristics instead of true understanding. For instance, in medical diagnosis, relying on superficial patterns could lead to misdiagnosis.
Dialogue Systems:  Chatbots or virtual assistants could exhibit inconsistent or illogical behavior in conversations, especially when dealing with novel situations or concepts. This could lead to frustrating user experiences and erode trust in the system.
Text Summarization: Summaries generated by LLMs might miss crucial information or introduce factual errors if the model fails to grasp the underlying semantic relationships between entities and events in the text.
To mitigate these risks, developers should:

Prioritize Robustness:  Focus on developing LLMs that are less susceptible to shallow heuristics and exhibit more reliable semantic understanding. This might involve exploring alternative training objectives, incorporating knowledge bases, or integrating symbolic reasoning components.
Thorough Evaluation:  Rigorously evaluate LLMs on tasks and datasets specifically designed to assess commonsense reasoning and semantic understanding. This includes testing for robustness to adversarial examples and unexpected inputs.
Human-in-the-Loop Systems:  Consider deploying LLMs in human-in-the-loop systems where human experts can monitor the model's outputs, provide feedback, and correct errors. This can help ensure accuracy and reliability in critical applications.
By acknowledging these implications and adopting appropriate development and deployment strategies, we can work towards LLMs that are more trustworthy and effective in real-world settings.