Context|prior knowledge interaction in language models

[2404.04633] Context versus Prior Knowledge in Language Models

Abstract:To answer a question, language models often need to integrate prior knowledge learned during pretraining and new information ...

Context versus Prior Knowledge in Language Models - ACL Anthology

Our work aims to make model behavior more transparent by providing a new tool to analyze the interaction between context and prior knowledge.

Context versus Prior Knowledge in Language Models - arXiv

The existence of entity bias naturally raises the question of how it interacts with context to shape a model's response. Longpre et al. (2021) ...

Context versus Prior Knowledge in Language Models - Linnk AI

Language models integrate prior knowledge and new contextual information in predictable ways, relying more on prior knowledge for familiar entities and ...

What is In-context Learning, and how does it work - Lakera AI

Using examples in natural language serves as an interface for interaction with large language models(LLMs). This framework simplifies the ...

Context versus Prior Knowledge in Language Models - AIModels.fyi

This paper investigates how language models balance using context versus relying on their prior knowledge when making predictions.

Larger language models do in-context learning differently

We found that overriding prior knowledge is an emergent ability of model scale, as is the ability to learn in-context with semantically-unrelated labels.

Context-prior knowledge interaction in language models - Linnk AI

Language models integrate prior knowledge and new contextual information in predictable ways, relying more on prior knowledge for familiar entities and being ...

Understanding In-Context Learning for Language Models - Medium

In-context learning is the ability of an AI model to generate responses or make predictions based on the specific context provided to it.

What is In-context Learning, and how does it work - Floatbot.AI

In context learning is a feature of Large language models (LLMs). Basically, you give the model examples of what you want it to do (via prompts)

How in-context learning improves large language models

One powerful ability is at least partly responsible for their growing popularity: in-context learning, which enriches a model with examples ...

[D] LLMs: Why does in-context learning work? What exactly ... - Reddit

In context learning is a feature of Large language models (LLMs). Basically, you give the model examples of what you want it to do (via ...

Language Models can Exploit Cross-Task In-context Learning for ...

To study the interaction between heterogeneous ... To answer the questions, logical reasoning abilities and in some cases prior knowledge are ...

Supervised Knowledge Makes Large Language Models Better In ...

Large Language Models (LLMs) exhibit emerging in-context learning abilities through prompt engineering. The recent progress in large-scale ...

Symbol tuning improves in-context learning in language models

A key feature of human intelligence is that humans can learn to perform new tasks by reasoning using only a few examples.

Large language models reduce public knowledge sharing on online ...

This substitution threatens the future of the open web, as interactions with AI models are not added to the shared pool of online knowledge. ... prior to the ...

Context Window of Language Models | by Rahul S - Medium

If the interaction exceeds the context window, the model loses access to the earliest parts of the conversation. This limitation can impact the ...

Evaluating large language models in analysing classroom dialogue

Similar forms of dialogue have been highlighted in the context of student–student interaction. ... prior knowledge or beliefs and the wider ...

Compressed Context Memory for Online Language Model Interaction

Through evaluations on conversation, personalization, and multi-task learning, we demonstrate that our approach achieves the performance level ...

Measuring the importance of context when modeling language ...

Lastly, given that we have designed a flexible framework for predicting a given aspect of language understanding using advanced representations ...