Investigating Lexical Semantics in Generative LLMs

Core Concepts

The author explores how different layers of generative LLMs encode lexical semantics and prioritize prediction tasks, shedding light on the interaction between understanding and prediction in these models.

Abstract

The study delves into the evolution of lexical semantics in large language models, focusing on the bottom-up approach to hidden states. It contrasts generative LLMs like Llama2 with discriminative models like BERT, highlighting the encoding of lexical semantics in lower layers for prediction. The research emphasizes practical insights into utilizing hidden states for understanding word meanings and offers guidance on interpreting representations in LLMs.

Stats

Large language models have achieved remarkable success in general language understanding tasks.
Lower layers encode lexical semantics, while higher layers prioritize prediction tasks.
BERT-like models exhibit subpar performance in downstream tasks compared to GPT-like models.
The WiC dataset is used as a proxy task for exploring lexical semantics.
Llama2 achieves comparable results to bidirectional BERT models.
Nouns generally exhibit higher accuracy than verbs across different settings and models.

Quotes

"Our experiments show that the representations in lower layers encode lexical semantics, while the higher layers are responsible for prediction."
"The trend contrasts with bidirectional BERT_large model, which obtains the best performance in higher layers."
"This hierarchical behavior suggests a dynamic interaction between understanding and prediction in generative LLMs."

Key Insights Distilled From

Fantastic Semantics and Where to Find Them

by Zhu Liu,Cunl... at arxiv.org 03-05-2024

https://arxiv.org/pdf/2403.01509.pdf

Deeper Inquiries

How do different languages and models affect the estimation of lexical semantics?

Different languages and models can have varying effects on the estimation of lexical semantics. When it comes to languages, factors such as morphology, syntax, and semantic structures unique to each language can influence how words are represented and understood within a model. For instance, languages with rich inflectional systems may require more complex representations to capture nuances in meaning compared to languages with simpler grammatical structures.
Additionally, the availability and quality of linguistic resources for different languages play a crucial role in training language models. Languages with limited resources may not benefit from pretraining on large datasets or fine-tuning on specific tasks as effectively as widely spoken languages like English. This disparity can impact the accuracy and generalization capabilities of models when estimating lexical semantics across diverse linguistic contexts.
Moreover, the architecture and design choices of language models themselves can also impact how well they estimate lexical semantics across different languages. Models that rely heavily on context or have biases towards certain linguistic features may struggle to accurately capture semantic nuances present in less common or structurally distinct languages.
In summary, understanding how different languages interact with various model architectures is essential for improving the estimation of lexical semantics across multilingual settings.

How can future studies bridge the gap between high-dimensional vectors from computational models and low-dimensional concepts from linguistic conventions?

Bridging the gap between high-dimensional vectors generated by computational models like large language models (LLMs) and low-dimensional concepts from linguistic conventions poses an important challenge for future research aiming to enhance interpretability and usability of these models.
One approach is through interpretability techniques that map high-dimensional vector spaces into lower dimensions while preserving meaningful relationships among words or tokens. Dimensionality reduction methods such as Principal Component Analysis (PCA) or t-SNE can help visualize embeddings in a more human-interpretable manner without losing critical information encoded in higher dimensions.
Another strategy involves leveraging insights from linguistics to inform model design and evaluation. By incorporating linguistic principles such as word sense disambiguation rules or syntactic constraints into training objectives or probing tasks, researchers can guide computational models towards learning representations that align more closely with traditional linguistic concepts.
Furthermore, developing hybrid approaches that combine symbolic reasoning frameworks with neural network-based representations could offer a pathway to reconcile high-dimensional vector spaces with low-level linguistic abstractions effectively. Integrating structured knowledge graphs or ontologies alongside dense embeddings might facilitate better alignment between computational outputs and human-understandable concepts.
Overall, interdisciplinary collaboration between experts in natural language processing, machine learning, cognitive science, linguistics, and related fields will be instrumental in bridging this gap successfully by drawing upon diverse perspectives and methodologies.

What are the potential ethical considerations related to probing semantic representations in large language models?

Probing semantic representations in large language models raises several ethical considerations that researchers must address responsibly:

Privacy Concerns: Probing often involves analyzing internal model states which could inadvertently reveal sensitive information embedded within text data used for training these models. Safeguarding user privacy by anonymizing data sources or implementing strict access controls becomes paramount during probing experiments.

Bias Amplification: Uncovering biases present within LLMs' semantic representations through probing could lead to unintended reinforcement if not appropriately mitigated post-analysis. Addressing bias amplification requires proactive measures like debiasing techniques during both training phases as well as downstream applications utilizing these learned embeddings.

Model Misinterpretation: Interpreting probe results incorrectly might result in mischaracterizing LLM behaviors leading to erroneous assumptions about their capabilities or limitations.

4 .Fairness Issues: Ensuring fairness throughout all stages of probing experiments including dataset selection bias mitigation strategies helps prevent perpetuating inequalities present within existing datasets further exacerbating societal disparities.
5 .Transparency & Accountability: Providing clear documentation regarding probing methodologies findings promotes transparency accountability ensuring reproducibility validity research outcomes fostering trust amongst stakeholders involved analysis process
6 .Unintended Consequences: Changes made based probe results should carefully evaluated mitigate potential negative impacts downstream applications deployment new insights derived probes
By addressing these ethical considerations proactively integrating responsible practices into every stage probing research cycle researchers contribute building trustworthy sustainable AI technologies benefit society at large