What are the best NLP models for question answering?

NLP models for question answering have evolved significantly with the rise of Large Language Models (LLMs). Since LLMs use generative models and have been trained on extensive data these models are adopted widely after fine-tuning for specific context. fine-tuning LLMs for specific contexts can be resource-intensive and because of this reason, RAG has been introduced by seamlessly combining generative AI and extractive features. RAG efficiently extracts information from a database based on user prompts, providing a tailored prompt to LLMs. This enables the LLM to extract relevant information with a comprehensive contextual understanding, utilizing both generative and extractive functionalities.

There are several state-of-the-art NLP models for question answering (QA), including: 1. BERT (Bidirectional Encoder Representations from Transformers) 2. GPT (Generative Pre-trained Transformer) 3. XLNet (Generalized Autoregressive Pretraining for Language Understanding) 4. RoBERTa (Robustly optimized BERT approach) 5. T5 (Text-To-Text Transfer Transformer) Each of these models has its strengths and weaknesses, and the best one for a particular task depends on factors such as the nature of the questions and the available resources.

When it comes to NLP models for question answering (QA), there's a bit of a split in how they approach the task. Think of it like this: imagine you're trying to find an answer in a book. With extractive models, it's like scanning through the book to find the exact paragraph or sentence that directly answers your question. These models look for matching text passages in a given document and pull those out as the answer. On the other hand, generative models take a more creative approach. It's as if they read through the entire book, understand the context, and then craft a new response that synthesizes information from different parts of the book to answer your question.

There isn't a universally "best" NPL (Non-Performing Loans) model, as the effectiveness of a model depends on various factors such as the specific context, data availability, and the objectives of the analysis. However, some commonly used NPL models include logistic regression, decision trees, random forests, neural networks, and support vector machines. The choice of model should be based on its performance in accurately predicting NPLs and its suitability for the available data and resources. Additionally, ensemble methods combining multiple models can often improve predictive accuracy.

Generative AI models such as Google foundation models and open source model such as Openllama and T5 are good for question answering when implemented in RAG architecture.

Question answering system can be designed by using the traditional or transformers models. Traditional NLP method- - Query based Summarization can be used for retrieving the relevant answers. Search engine can be used for searching relevant documents & then extractive summery can be generated for retrieving the answers. - QA can also be retrieved using knowledgebase & ontological resources. We need to parse the input query & convert it into SPARQL query. Transformers model- Bert is a encoder-only model which is designed for NLU tasks like NER, Question answering system etc. We can use the pre-trained BERT model. We can also train it using QA & Squad dataset. One word or short answer can also be retrieved using RNN & LSTM algo.

Extractive NLP models, such as BERT, excel in question answering by selecting relevant text snippets based on bidirectional contextual understanding. While BERT's advantage lies in its comprehensive context grasp, a drawback is its computational intensity, posing challenges for deployment in resource-constrained environments.

Extractive QA models are superb when a direct answer exists within a body of text. They locate and highlight the relevant text portion, offering efficient, verbatim answers ideal for fact-based queries. However, if a question requires reasoning, understanding deeper connections within the text, or generating a summary, generative models may prove more suitable. These models can synthesize answers not explicitly present in the text, demonstrating a greater degree of understanding. Ultimately, the "best" approach for QA depends heavily on the specific task and type of questions being asked.

Extractive question-and-answer (Q&A) models search for the exact portion of a text that directly answers a question. They work by understanding the question and the text, scanning the text for the answer, and scoring different parts of the text based on their relevance to the question. Extractive models are often fast and reliable when the answer is clearly present in the text but may struggle with questions that require a deeper understanding of the information or when the answer is not explicitly stated.

In this variant, the model extracts the answer directly from a context. The context can be a provided text, a table, or even HTML. BERT-like models are commonly used for extractive QA. Example: Given a passage about the Amazon rainforest, an extractive QA model can find the answer to the question, "Which name is also used to describe the Amazon rainforest in English?"

LLMs. Generative models so far have shown the best performance. Transformers trained on big data to obtain the knowledge plus learning the NLP rules, then fine tuned to play in Q&A scenarios. ChatGPT is not flawless, but, generally speaking, excellent and beyond our former expectations of an AI connectionist NLP system. I would call it a system not a model because of a few points. Now, for researchers the Pandora box is open. They may try white box LLMs and develop capable QA models. For them options are abondunt. Lamma, T5, etc. Everyweek we will see one more. For users, I still prefer ChatGPT, even based on GPT 3.5.

I've experimented with various AI writing models, starting with GPT-3's detailed but lengthy responses, then Claude-2 for its brevity yet limited adaptability, and BARD's hit-or-miss nature. GPT-4 brought promise with refined prompts, though custom models didn't echo my style as hoped. Gemini's responses felt too artificial, lacking simplicity. Meta's GenAI model integration in apps didn't work for me, either. Each model's unique traits underscore the diverse AI landscape, showing there's still room for improvement in personalizing AI to user preferences.

In opensource, Lean models Llama & T5-Flan are the most promising models. In propreitary one, openAI and Gemini are the top-2.

Generative models, like T5 (Text-To-Text Transfer Transformer), redefine question answering by generating answers as sequences of tokens, even if not explicitly present in the source text. T5 employs a transformer encoder-decoder architecture, trained on extensive text data and fine-tuned across NLP tasks, including QA. It processes the question and source text as input, generating the answer as an output sequence. T5's versatility enables it to tackle open and closed-domain QA, offering both abstractive and extractive capabilities, marking a significant advancement in NLP.

Generative models for question answering offer versatility by generating answers based on learned patterns from vast data. Unlike extractive models, they create novel responses, useful for paraphrasing and creative language use. However, they may struggle with accuracy and coherence. Advancements aim to address these challenges, pushing the boundaries of their capabilities in natural language processing tasks.

I feel ranking and human-based feedback loops play a crucial role too. Ranking helps prioritize answers based on their relevance and quality, mimicking how humans would choose the best answer from a list of options. The human feedback loop is essential for fine-tuning the model based on real-world usage and human judgment, ensuring that it continues to improve and adapt to the intricacies of language and context.

There are several metrics for question answering task in NLP, these metrics are split into 2 groups, simple and advanced. The simple group contains metrics that most professionals are familiar with such as, Exact Match, Recall, Precision, F1 score, Error Rate, Average Precision, Mean Average Precision, and Mean Reciprocal Rank. The advanced group contains relatively less metrics, in addition to their variations. The advanced metrics include BLUE, NIST, ROUGE, and METEOR scores. The mentioned advanced scores generally uses a combination of simple metrics to generate a score for answer relevance evaluation against the ground truth. It is worth mentioning that, these scores could increase as researches find new ways of evaluating answers.

Evaluation metrics form the cornerstone of determining the best NLP model for QA tasks. Different models may excel in various aspects of answering questions. Some prioritize finding the exact text span within a passage, while others focus on generating a concise, human-like answer. Metrics like Exact Match (EM), F1-score, and BLEU provide distinct lenses for measurement. EM checks for verbatim matches, F1-score balances precision and recall, and BLEU assesses similarity to human-generated answers. Thoroughly comparing these metrics across models, in context of the specific QA task, is crucial for identifying the most suitable approach.

Evaluating QA models involves assessing answer accuracy and relevance. Exact Match (EM) rates perfect answer matches, while F1 score harmonizes precision and recall of answer spans against true answers. BLEU score compares predicted and reference answers via n-gram similarity, and ROUGE score focuses on recall, evaluating similarity based on longest common subsequences. These metrics ensure QA models' answers are not only accurate but also contextually aligned with the ground truth.

EM, F1, BLEU, and ROUGE, each offer valuable insights but focus on different aspects: - Exact Match (EM): Strict and useful for factual questions, but prone to penalizing paraphrases or alternative wordings. - F1 Score: Balances precision and recall, capturing both correctness and completeness. However, it might not differentiate well between answers with varying levels of detail. - BLEU Score: Emphasizes n-gram overlap, focusing on fluency and similarity to reference answers. Can be insensitive to factual correctness. - ROUGE Score: Similar to BLEU but prioritizes recall, rewarding answers that capture key information from the reference. Might favor longer answers even if not entirely accurate.

Semantik ist nicht messbar! So individuell wie der Sprachgebrauch bei jedem Menschen ist, so schwer ist es Semantik objektiv zu vergleichen. Es ist sogar unm?glich. Auch wenn bei der semantischen Suche mit k-nearest neighbors vermeintlich passende Inhalte als Kontext für Sprachmodelle identifiziert werden, so ist und bleibt eine Interpretation der Ergebnisse unm?glich. Wie kann ein Mensch einen hunderte Dimensionen umfassenden Raum interpretieren? Kurzum: überhaupt nicht. Aber es gibt gro?e Bestrebungen die Interpretierbarkeit zu verbessern! Zwar gehen bei der Dimensionsreduktion Informationen verloren, Techniken wie UMAP erm?glichen aber immerhin eine visuelle Interpretation für den Menschen und damit ein zus?tzliches Quality Gate.

some of the challenges and limitations faced by Natural Language Processing (NLP) models when it comes to question answering: 1) Long-Form Question Answering (LFQA): 2) Evaluation Metrics and Benchmarks: 3) Maintaining Context Over Time: 4) Quality of Input Data: 5) Understanding Human Emotions and Precision the ability to interpret emotions accurately 6) High Computing Resources. 7) Lack of Unifying Ontology. 8) Limited Practical Understanding: NLP is a powerful tool, but addressing these challenges will drive further advancements in question answering systems!

For QA tasks, BERT and GPT-3 excel. BERT is great for extractive QA, pulling answers directly from text, while GPT-3 can infer answers, even if not plainly stated. They're assessed on metrics like F1 score and Exact Match but face challenges like context understanding and data demands. Future QA models aim for more context-aware, continuous learning capabilities. Select models based on the task, data quality, and computational resources.

Lack of world knowledge: QA models heavily rely on the information present in their training data and pre-trained language models. Data biases and generalization: QA models can be influenced by biases present in their training data, leading to biased or unfair responses. Inability to ask clarifying questions: QA models typically lack the ability to ask clarifying questions to the user when faced with ambiguous queries or insufficient information. Adversarial attacks: NLP QA models are susceptible to adversarial attacks, where maliciously crafted input can manipulate or deceive the model into providing incorrect or biased answers.

The top NLP models for question answering (QA) include BERT, GPT, and their derivatives like BERT-large, RoBERTa, and T5. These models leverage large-scale pre-training on diverse text data to understand context and generate accurate responses to questions across various domains. However, challenges persist, such as contextual understanding, especially in complex or ambiguous queries. Additionally, QA models are susceptible to bias in training data, potentially leading to skewed or inaccurate answers. Overcoming these challenges involves continuous research and development efforts to improve model robustness, scalability, and fairness, ensuring reliable and unbiased performance in real-world applications.

The future direction of QA models, IMHO, clearly is towards multi-modal output, just like we have it from Google which throws multiple link in all the domains - images, news, videos etc. whenever we search for something. If there was a way in which the preceding activity/context of the user can be provided to the model, it will ensure that the user will get the right answer in the context of what they have been looking at. I know there are practical issues in what I am saying but then if I want to get the right answer/advice I need to tell more to the Omniscient Model.

One thing that I find fascinating (and that we don't talk about enough) is that future QA models are likely to make more extensive use of knowledge graphs and external databases, enabling them to pull in real-time data and detailed information that is not explicitly mentioned in the given text or media. This approach can significantly enhance the accuracy and relevance of answers, especially for fact-based questions or when current data is required. Imagine a scenario in personalized medicine, where treatment and care plans are tailored to individual patients by integrating vast amounts of data from diverse sources, like medical records, genetic information, research studies, and real-time health monitoring devices.

The clear winner would be multi-modal input. Ideally, also world-understanding but I believe we are quite a ways from it. We should likely know more on that when we get to know more about SoRa. I also understand the argument placed by Yann Lecun's JEPA. It's an exciting time to be in the space of NLP models and LLMs. Can sometime be overwhelming

In my perspective, the future of QA systems lies in integrating multiple modalities, expanding multilingual capabilities, and enhancing interactive dialogues. These advancements will enable QA systems to not only process a broader range of information, including images and videos but also provide more accurate and comprehensive answers. Additionally, I believe that the development of conversational interactions will create a more engaging user experience, while the incorporation of external knowledge sources and advanced reasoning will improve the precision of responses. Overall, these innovations will significantly enhance the utility and effectiveness of QA systems as tools for information retrieval and learning.

The future of quality assurance promises exciting advances: Models could integrate multimodal inputs such as images and videos and support multilingual interactions. Improved user interaction through conversational interfaces could enable models to ask questions or provide feedback, enriching the QA experience. In addition, the integration of external knowledge bases and advanced inference capabilities could significantly improve the accuracy and depth of answers provided by QA systems.

LLMs are a big leap forward in understanding and working with human language using computers, but I don’t think they have made the older ways of doing things in NLPs useless. The older, traditional NLP methods still have their place, especially when you need something more straightforward, specialised, or when you’re constrained on compute. The best approach is to use a mix of both these new LLMs and the classic NLP techniques to get the job done.

In the dynamic field NLP, QA models have evolved into two main categories: extractive models, like BERT, efficiently extract specific text spans for factual queries, while generative models, exemplified by T5, excel in producing human-like responses through sequence generation. Evaluation metrics, including Exact Match, F1 score, BLEU, and ROUGE, play a crucial role in assessing QA model performance. Challenges persist, ranging from dataset creation complexities to the model's ability to generalize and provide clear explanations.

As a Psychologist who works in Machine Learning, I love exploring Mythology as espoused by Jungian psychology. Our behaviors often stem from the unconscious mind (think gut feelings). This aligns with the top-down/bottom-up approach in neuroscience. Exploring the multi-dimensional nature of the mind could be the key to unlock NLP. Specifically, mythology offers a treasure trove of symbols that resonate deeply within the collective unconscious. By incorporating these elements into NLP training data, we can potentially equip language models with a richer understanding of the nuances and emotional undercurrents woven into human communication. For instance exploring "trickster" archetype could improve its capacity to detect irony and deception.

One of the considerations is also to factor in sentiment and contextual analysis. While the baseline models now are able to provide good outputs, further refinement is often needed to consider domain specific keywords (especially in industries like legal or engineering with very specific words to use in certain context). As such, separate training and modelling may be required.

IMHO, the regulatory bodies in healthcare and legal should seriously start considering how to allow AI driven QA models to provide medical or legal advice. The current problem is that these AI models, cannot explain how they arrived at a diagnosis/prescription (I am calling it an answer to a question) and it precludes them from being used extensively. We need some framework around this otherwise we are losing out on one big benefit that we can reap from AI.