Large Language Models for Information Extraction and Information Retrieval

Introduction

Introduction

My name is Stuart Milton, and I have been invited to give an introductory talk about Large Language Models (LLMs) and my research in the areas of information extraction and information retrieval. In this article, I will offer an overview of LLMs, delve into information extraction including multitask learning, graph convolutional networks, and in-context learning, and finally touch upon augmented question-answering systems. I will conclude with a brief discussion on a couple of my projects.

Understanding Large Language Models

Large language models have become a focal point in natural language processing (NLP) and have demonstrated remarkable capabilities in various fields, including information retrieval. These models are predominantly categorized into two types:

1. Mask-based Models: These are typically represented by models like BERT with fewer than a billion parameters, designed to predict masked tokens in free text. These models excel at classification tasks and can be fine-tuned for specific downstream applications.

2. Generative Models: This group is typified by the GPT series. Starting with GPT-1 in 2017 and exemplified by models like GPT-3 with 175 billion parameters, these models have begun to generate human-quality text and exhibit capabilities in dialogue, question answering, summarization, and basic reasoning.

While LLMs have garnered attention, they do have certain challenges, including hallucinations, information loss, bias in training data, and uncertainty in their outputs. The question arises as to whether generative models could potentially replace traditional search engines altogether.

Research Focus: Information Extraction

My primary research focuses on leveraging LLMs for information extraction. A noteworthy aspect of my research includes multitask learning, where models are trained on multiple related tasks, improving performance compared to single-task models.

Example 1: Mental Health Text Analysis

In previous work, we analyzed posts from mental health forums to identify changes in mood and suicide risk classification. We used embeddings from several models, including RoBERTa and FastText. Even with modest F1 scores nearing 0.60 for mood change detection, the method served as a useful triage tool for mental health providers.

Example 2: Graph Convolutional Networks

We incorporated graph convolutional networks to enhance our models further. This approach leveraged graph representations of conversation interactions, emotions, and sentiments, thus enriching our feature set. This model achieved an impressive F1 score of 0.85.

Example 3: Evidence Extraction and Summarization

In recent work focusing on suicide evidence extraction, we utilized in-context learning, where examples were provided directly in the input text for the model. This approach led to a significant improvement, achieving an F1 score of 0.92 and generating human-explained outputs from classified posts.

Information Retrieval and Augmented Question Answering

My upcoming research aims to explore retrieval-augmented question-answering systems, using models like DSE passage retriever and experimenting with different retrieval methods. This may involve both retrieval-based generation and generation from scratch, relying on LLMs to create relevant documents.

Conclusion and Future Directions

In conclusion, advances in LLMs hold immense potential for applications in information extraction and retrieval. The models are rapidly evolving, and our research continues to demonstrate their effectiveness in tackling complex challenges, especially in sensitive domains like mental health.

Keywords

Large Language Models, Information Extraction, Information Retrieval, Multitask Learning, Graph Convolutional Networks, In-context Learning, Augmented Question Answering, Suicide Risk Classification, Mental Health Analysis.

FAQ

Q1: What are Large Language Models?
A1: Large Language Models are advanced algorithms designed to understand and generate human-like text based on training data, demonstrating capabilities in various natural language processing tasks.

Q2: How do Mask-based models differ from Generative models?
A2: Mask-based models like BERT are pre-trained on tasks involving predicting masked tokens, while Generative models like GPT are trained to generate coherent text based on prompts.

Q3: What are some challenges associated with Large Language Models?
A3: Challenges include hallucinations (generating false information), information loss during summarization, biases from training data, and uncertainty in outputs.

Q4: How can Large Language Models be applied in mental health research?
A4: They can be employed to analyze text data from mental health forums to classify posts, detect mood changes, and assess suicide risk effectively.

Q5: What is in-context learning?
A5: In-context learning refers to providing examples directly within the input text to help the model understand the desired output better without extensive fine-tuning.