betekintés - Language model evaluation - # Pronoun use fidelity

Evaluating Pronoun Use Fidelity in Large Language Models: Assessing Reasoning, Repetition, and Bias

Q: How can we encourage language models to explicitly track individuals and their pronouns, similar to how humans approach this task?

To encourage language models to explicitly track individuals and their pronouns, similar to how humans approach this task, we can consider the following strategies: Explicit Coreference Resolution: Provide models with explicit coreference resolution tasks where they have to link pronouns to specific entities in a given context. By training models on tasks that require them to track individuals and their pronouns accurately, we can improve their ability to understand and use pronouns in context. Chain-of-Thought Prompting: Implement prompting techniques that encourage models to maintain a chain of thought throughout a conversation or text. By guiding models to remember and refer back to previously mentioned entities and their associated pronouns, we can help them track individuals more effectively. Multi-turn Conversational Training: Train models on multi-turn conversational datasets where pronouns are used in a coherent and consistent manner across different utterances. This can help models learn the context of pronoun usage and improve their ability to track individuals and their pronouns over extended dialogues. Fine-tuning on Pronoun Use Fidelity Tasks: Fine-tune models specifically on tasks related to pronoun use fidelity, where they are required to reuse pronouns accurately in given contexts. By providing targeted training on this aspect of language understanding, models can learn to track individuals and their pronouns more effectively. Incorporating Real-World Knowledge: Integrate external knowledge bases or commonsense reasoning capabilities into language models to help them understand the real-world relationships between individuals and their associated pronouns. By grounding models in real-world knowledge, they can better track individuals and their pronouns in context.

Q: How do the patterns of pronoun use fidelity observed in this study relate to broader questions of language model grounding and reasoning about the real world?

The patterns of pronoun use fidelity observed in this study shed light on the challenges language models face in grounding their understanding of pronouns in real-world contexts. These patterns have broader implications for language model reasoning about the real world in the following ways: Contextual Understanding: The study highlights the importance of context in pronoun usage and the challenges models face in maintaining this context over multiple sentences. This relates to broader questions of how language models understand and reason about the context of a given text or conversation. Robustness to Distractions: The study shows that language models struggle with distractions when reasoning about pronouns, leading to errors in pronoun use fidelity. This relates to broader questions of how models can maintain focus and reasoning coherence in the presence of irrelevant information or distractors. Bias and Fairness: The study reveals disparities in model performance across different pronouns, indicating potential biases in pronoun usage. This connects to broader questions of bias and fairness in language models and their impact on marginalized groups or individuals with non-binary pronouns. Generalization and Adaptation: The findings suggest that models may struggle with generalizing their understanding of pronouns to new or complex scenarios. This raises questions about how models can adapt their reasoning capabilities to novel contexts and improve their generalization abilities. Interpretability and Explainability: Understanding the patterns of pronoun use fidelity can also inform efforts to make language models more interpretable and explainable. By analyzing how models reason about pronouns, we can gain insights into their decision-making processes and improve transparency in model behavior.

Q: What architectural or training modifications could make language models more robust to distractions when reasoning about pronouns?

To make language models more robust to distractions when reasoning about pronouns, several architectural or training modifications can be considered: Attention Mechanisms: Enhancing attention mechanisms to focus on relevant information and filter out distractions in the input data. This can help models prioritize important context for pronoun resolution and reduce the impact of distractors. Memory Augmented Models: Incorporating memory-augmented architectures that allow models to store and retrieve relevant information over multiple steps. This can help models maintain context and track individuals and their pronouns more effectively in the presence of distractions. Adversarial Training: Training models with adversarial examples that introduce distractors and encourage models to focus on the most salient information for pronoun resolution. This can help models learn to ignore irrelevant distractions and improve their robustness to noisy input. Multi-task Learning: Training models on a combination of tasks related to pronoun resolution, coreference resolution, and context tracking. By exposing models to diverse training data and tasks, they can learn to handle distractions and maintain context more effectively. Fine-tuning Strategies: Implementing fine-tuning strategies that specifically target distractions in pronoun resolution tasks. By fine-tuning models on datasets with varying levels of distractions, models can learn to adapt to different levels of noise in the input data. Regularization Techniques: Applying regularization techniques such as dropout or weight decay to prevent overfitting to distractors and encourage models to focus on the most relevant information for pronoun resolution. By incorporating these architectural and training modifications, language models can improve their ability to reason about pronouns in the presence of distractions and enhance their overall robustness in understanding and using pronouns in context.

Alapfogalmak

Large language models struggle to robustly and faithfully reason about pronouns, even in simple settings, and continue to amplify discrimination against users of certain pronouns.

Kivonat

The authors introduce the task of pronoun use fidelity, which evaluates whether language models can correctly reuse a previously-specified pronoun in a later sentence, independent of potential distractors. They present a carefully-designed dataset of over 5 million instances to evaluate this task in English across 37 popular large language models.

The key findings are:

While models can mostly faithfully reuse previously-specified pronouns in the absence of distractors, they are significantly worse at processing she/her/her, singular they, and neopronouns.
Models are not robustly faithful to pronouns, as they are easily distracted. With even one additional sentence containing a distractor pronoun, accuracy drops on average by 34%. With 5 distractor sentences, accuracy drops by 52% for decoder-only models and 13% for encoder-only models.
Encoder-only models are better at faithfully reusing pronouns than decoder-only models of the same scale, and they are also more robust to the addition of distractor sentences. Most model errors are due to distraction, but with additional distractors, encoder-only models become less distracted while decoder-only models get even more distracted.

The authors conclude that widely-used large language models are unable to robustly and faithfully reason about pronouns in a simple setting that is easy for humans, and they encourage researchers in bias and reasoning to address these performance gaps.

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arxiv.org

Statisztikák

The accountant had just eaten a big meal so her stomach was full.
The taxpayer needed coffee because their day had started very early.
Their sleep had been fitful.
The accountant was asked about ___ charges for preparing tax returns.

Idézetek

"Robust, faithful and harm-free pronoun use for individuals is an important goal for language models as their use increases, but prior work tends to study only one or two of these components at a time."
"We find that while models can mostly faithfully reuse previously-specified pronouns in the presence of no distractors, they are significantly worse at processing she/her/her, singular they and neopronouns."
"With even one additional sentence containing a distractor pronoun, accuracy drops on average by 34%. With 5 distractor sentences, accuracy drops by 52% for decoder-only models and 13% for encoder-only models."

Főbb Kivonatok

Robust Pronoun Use Fidelity with English LLMs

by Vagrant Gaut... : arxiv.org 04-05-2024

https://arxiv.org/pdf/2404.03134.pdf

Robust Pronoun Use Fidelity with English LLMs

Mélyebb kérdések

How can we encourage language models to explicitly track individuals and their pronouns, similar to how humans approach this task?

To encourage language models to explicitly track individuals and their pronouns, similar to how humans approach this task, we can consider the following strategies:

Explicit Coreference Resolution: Provide models with explicit coreference resolution tasks where they have to link pronouns to specific entities in a given context. By training models on tasks that require them to track individuals and their pronouns accurately, we can improve their ability to understand and use pronouns in context.

Chain-of-Thought Prompting: Implement prompting techniques that encourage models to maintain a chain of thought throughout a conversation or text. By guiding models to remember and refer back to previously mentioned entities and their associated pronouns, we can help them track individuals more effectively.

Multi-turn Conversational Training: Train models on multi-turn conversational datasets where pronouns are used in a coherent and consistent manner across different utterances. This can help models learn the context of pronoun usage and improve their ability to track individuals and their pronouns over extended dialogues.

Fine-tuning on Pronoun Use Fidelity Tasks: Fine-tune models specifically on tasks related to pronoun use fidelity, where they are required to reuse pronouns accurately in given contexts. By providing targeted training on this aspect of language understanding, models can learn to track individuals and their pronouns more effectively.

Incorporating Real-World Knowledge: Integrate external knowledge bases or commonsense reasoning capabilities into language models to help them understand the real-world relationships between individuals and their associated pronouns. By grounding models in real-world knowledge, they can better track individuals and their pronouns in context.

How do the patterns of pronoun use fidelity observed in this study relate to broader questions of language model grounding and reasoning about the real world?

The patterns of pronoun use fidelity observed in this study shed light on the challenges language models face in grounding their understanding of pronouns in real-world contexts. These patterns have broader implications for language model reasoning about the real world in the following ways:

Contextual Understanding: The study highlights the importance of context in pronoun usage and the challenges models face in maintaining this context over multiple sentences. This relates to broader questions of how language models understand and reason about the context of a given text or conversation.

Robustness to Distractions: The study shows that language models struggle with distractions when reasoning about pronouns, leading to errors in pronoun use fidelity. This relates to broader questions of how models can maintain focus and reasoning coherence in the presence of irrelevant information or distractors.

Bias and Fairness: The study reveals disparities in model performance across different pronouns, indicating potential biases in pronoun usage. This connects to broader questions of bias and fairness in language models and their impact on marginalized groups or individuals with non-binary pronouns.

Generalization and Adaptation: The findings suggest that models may struggle with generalizing their understanding of pronouns to new or complex scenarios. This raises questions about how models can adapt their reasoning capabilities to novel contexts and improve their generalization abilities.

Interpretability and Explainability: Understanding the patterns of pronoun use fidelity can also inform efforts to make language models more interpretable and explainable. By analyzing how models reason about pronouns, we can gain insights into their decision-making processes and improve transparency in model behavior.

What architectural or training modifications could make language models more robust to distractions when reasoning about pronouns?

To make language models more robust to distractions when reasoning about pronouns, several architectural or training modifications can be considered:

Attention Mechanisms: Enhancing attention mechanisms to focus on relevant information and filter out distractions in the input data. This can help models prioritize important context for pronoun resolution and reduce the impact of distractors.

Memory Augmented Models: Incorporating memory-augmented architectures that allow models to store and retrieve relevant information over multiple steps. This can help models maintain context and track individuals and their pronouns more effectively in the presence of distractions.

Adversarial Training: Training models with adversarial examples that introduce distractors and encourage models to focus on the most salient information for pronoun resolution. This can help models learn to ignore irrelevant distractions and improve their robustness to noisy input.

Multi-task Learning: Training models on a combination of tasks related to pronoun resolution, coreference resolution, and context tracking. By exposing models to diverse training data and tasks, they can learn to handle distractions and maintain context more effectively.

Fine-tuning Strategies: Implementing fine-tuning strategies that specifically target distractions in pronoun resolution tasks. By fine-tuning models on datasets with varying levels of distractions, models can learn to adapt to different levels of noise in the input data.

Regularization Techniques: Applying regularization techniques such as dropout or weight decay to prevent overfitting to distractors and encourage models to focus on the most relevant information for pronoun resolution.

By incorporating these architectural and training modifications, language models can improve their ability to reason about pronouns in the presence of distractions and enhance their overall robustness in understanding and using pronouns in context.