approfondimento - Language Models - # Controlling Hallucinations in Language Models

Understanding Language Model Hallucinations in Unfamiliar Scenarios

Q: How do real-world queries within a spectrum of partial familiarity impact language model predictions?

In the context of language models, real-world queries that fall within a spectrum of partial familiarity can have varying impacts on model predictions. When faced with partially familiar queries, language models may exhibit different levels of confidence and accuracy in their responses based on the extent to which they are familiar with the concepts or information being requested. For queries that are moderately familiar to the language model, it may be able to provide more accurate and relevant responses by drawing upon its pretraining data and finetuning examples. In these cases, the model is likely to generate responses that align closely with the input query and provide factually correct information. On the other hand, when presented with queries that lie at the edges of familiarity or are completely unfamiliar to the model, there is a higher likelihood of hallucinations or inaccuracies in generated responses. Language models tend to default towards generating plausible-sounding but factually incorrect answers when faced with unfamiliar inputs. This behavior stems from how finetuned models handle unfamiliar examples during training and testing. The impact of partial familiarity on language model predictions underscores the importance of understanding how models navigate uncertainty and make decisions based on their internal representations. By studying this phenomenon, researchers can develop strategies to improve model performance across various levels of query familiarity.

Q: What are the challenges and implications of extending factuality finetuning to more general prompted generation tasks?

Extending factuality finetuning techniques beyond specific applications like biography generation or multiple-choice answering poses several challenges and implications: Scalability: Factuality finetuning often requires specialized supervision for controlling hallucinations in long-form text generation tasks. Extending these techniques to more general prompted generation tasks would require scalable methods for providing feedback or rewards without relying heavily on human annotation. Diversity: Generalizing factuality finetuning across different types of prompts introduces challenges related to diverse content domains, languages, or genres where factual correctness may vary significantly. Evaluation: Assessing factuality in long-form text generated by language models for varied prompts can be complex due to subjective interpretations or domain-specific knowledge requirements. Adaptability: Adapting existing factuality control mechanisms designed for specific tasks might not directly translate well into broader contexts without considering task-specific nuances. Model Performance: Ensuring consistent improvements in factual accuracy while maintaining fluency and coherence across diverse prompt types presents a significant challenge in extending factuality fine-tuning approaches.

Q: How can external knowledge sources be effectively incorporated into reward models to mitigate hallucinations?

Incorporating external knowledge sources into reward models offers a promising approach for mitigating hallucinations in large language models: Knowledge Graph Integration: Leveraging structured knowledge graphs allows reward models access to external facts during evaluation, enabling them to verify response accuracy against known information sources. Fact Verification Systems: Integrating automated fact-checking systems as part of reward modeling helps validate generated responses against reliable databases or references. Semantic Similarity Measures: Using semantic similarity metrics between generated text and external resources aids in assessing response validity based on contextual relevance. 4Active Learning Strategies: Implementing active learning techniques where reward models dynamically seek clarification from external sources when uncertain about response accuracy enhances decision-making capabilities. 5Domain-Specific Knowledge Bases: Customizing reward functions using domain-specific knowledge bases ensures tailored evaluations aligned with task requirements.

Concetti Chiave

Large language models tend to default towards a hedged prediction when faced with unfamiliar inputs, leading to plausible but factually incorrect responses. By strategically manipulating the supervision of unfamiliar examples during finetuning, we can control how language models hallucinate.

Sintesi

Language models often generate plausible yet factually incorrect responses when queried on unfamiliar concepts. This work explores the mechanisms behind this behavior and proposes strategies to mitigate hallucinations. By adjusting the supervision of unfamiliar examples, models can be trained to admit uncertainty rather than fabricate information.
Key findings include:

Language models default towards a "hedged" prediction for unfamiliar inputs.
Strategic manipulation of finetuning data can steer model predictions towards more desirable responses.
RL approaches with conservative reward models show promise in improving factuality in long-form generation tasks.
Experimental results demonstrate that RL with conservative reward models outperforms standard SFT and RL with standard reward models in reducing hallucinations and generating more factual responses.

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Statistiche

Large language models tend to generate plausible-sounding yet factually incorrect responses when queried on unfamiliar concepts.
Finetuned language models default towards a hedged prediction for unfamiliar inputs.
Strategic manipulation of finetuning data can control how language models hallucinate.
RL approaches with conservative reward models improve factuality in long-form generation tasks.

Citazioni

"We explore the underlying mechanisms that govern how finetuned LLMs hallucinate." - Katie Kang et al.
"Our investigation reveals an interesting pattern: as inputs become more unfamiliar, LLM outputs tend to default towards a 'hedged' prediction." - Katie Kang et al.
"Our ultimate goal is to improve the factuality of long-form LLM generations." - Katie Kang et al.

Approfondimenti chiave tratti da

Unfamiliar Finetuning Examples Control How Language Models Hallucinate

by Katie Kang,E... alle arxiv.org 03-12-2024

https://arxiv.org/pdf/2403.05612.pdf

Unfamiliar Finetuning Examples Control How Language Models Hallucinate

Domande più approfondite

How do real-world queries within a spectrum of partial familiarity impact language model predictions?

In the context of language models, real-world queries that fall within a spectrum of partial familiarity can have varying impacts on model predictions. When faced with partially familiar queries, language models may exhibit different levels of confidence and accuracy in their responses based on the extent to which they are familiar with the concepts or information being requested.
For queries that are moderately familiar to the language model, it may be able to provide more accurate and relevant responses by drawing upon its pretraining data and finetuning examples. In these cases, the model is likely to generate responses that align closely with the input query and provide factually correct information.
On the other hand, when presented with queries that lie at the edges of familiarity or are completely unfamiliar to the model, there is a higher likelihood of hallucinations or inaccuracies in generated responses. Language models tend to default towards generating plausible-sounding but factually incorrect answers when faced with unfamiliar inputs. This behavior stems from how finetuned models handle unfamiliar examples during training and testing.
The impact of partial familiarity on language model predictions underscores the importance of understanding how models navigate uncertainty and make decisions based on their internal representations. By studying this phenomenon, researchers can develop strategies to improve model performance across various levels of query familiarity.

What are the challenges and implications of extending factuality finetuning to more general prompted generation tasks?

Extending factuality finetuning techniques beyond specific applications like biography generation or multiple-choice answering poses several challenges and implications:

Scalability: Factuality finetuning often requires specialized supervision for controlling hallucinations in long-form text generation tasks. Extending these techniques to more general prompted generation tasks would require scalable methods for providing feedback or rewards without relying heavily on human annotation.

Diversity: Generalizing factuality finetuning across different types of prompts introduces challenges related to diverse content domains, languages, or genres where factual correctness may vary significantly.

Evaluation: Assessing factuality in long-form text generated by language models for varied prompts can be complex due to subjective interpretations or domain-specific knowledge requirements.

Adaptability: Adapting existing factuality control mechanisms designed for specific tasks might not directly translate well into broader contexts without considering task-specific nuances.

Model Performance: Ensuring consistent improvements in factual accuracy while maintaining fluency and coherence across diverse prompt types presents a significant challenge in extending factuality fine-tuning approaches.

How can external knowledge sources be effectively incorporated into reward models to mitigate hallucinations?

Incorporating external knowledge sources into reward models offers a promising approach for mitigating hallucinations in large language models:

Knowledge Graph Integration: Leveraging structured knowledge graphs allows reward models access to external facts during evaluation, enabling them to verify response accuracy against known information sources.

Fact Verification Systems: Integrating automated fact-checking systems as part of reward modeling helps validate generated responses against reliable databases or references.

Semantic Similarity Measures: Using semantic similarity metrics between generated text and external resources aids in assessing response validity based on contextual relevance.

4Active Learning Strategies: Implementing active learning techniques where reward models dynamically seek clarification from external sources when uncertain about response accuracy enhances decision-making capabilities.
5Domain-Specific Knowledge Bases: Customizing reward functions using domain-specific knowledge bases ensures tailored evaluations aligned with task requirements.