large language models

Google Unveils Batch Calibration to Enhance LLM Performance

Google Research recently introduced a method termed Batch Calibration (BC) aimed at enhancing the performance of Large Language Models (LLMs) by reducing sensitivity to design decisions like template choice. This method is poised to address performance degradation issues and foster robust LLM applications by mitigating biases associated with template selections, label spaces, and demonstration examples. The unveiling took place on October 13, 2023, and the method was elucidated by Han Zhou, a Student Researcher, and Subhrajit Roy, a Senior Research Scientist at Google Research.

The Challenge

The performance of LLMs, particularly in in-context learning (ICL) scenarios, has been found to be significantly influenced by the design choices made during their development. The prediction outcomes of LLMs can be biased due to these design decisions, which could result in unexpected performance degradation. Existing calibration methods have attempted to address these biases, but a unified analysis distinguishing the merits and downsides of each approach was lacking. The field needed a method that could effectively mitigate biases and recover LLM performance without additional computational costs.

Batch Calibration Solution

Inspired by the analysis of existing calibration methods, the research team proposed Batch Calibration as a solution. Unlike other methods, BC is designed to be a zero-shot, self-adaptive (inference-only), and comes with negligible additional costs. The method estimates contextual biases from a batch of inputs, thereby mitigating biases and enhancing performance. The critical component for successful calibration as per the researchers is the accurate estimation of contextual bias. BC’s approach of estimating this bias is notably different; it relies on a linear decision boundary and leverages a content-based manner to marginalize the output score over all samples within a batch.

Validation and Results

The effectiveness of BC was validated using the PaLM 2 and CLIP models across more than 10 natural language understanding and image classification tasks. The results were promising; BC significantly outperformed existing calibration methods, showcasing an 8% and 6% performance enhancement on small and large variants of PaLM 2, respectively. Furthermore, BC surpassed the performance of other calibration baselines, including contextual calibration and prototypical calibration, across all evaluated tasks, demonstrating its potential as a robust and cost-effective solution for enhancing LLM performance.

Impact on Prompt Engineering

One of the notable advantages of BC is its impact on prompt engineering. The method was found to be more robust to common prompt engineering design choices, and it made prompt engineering significantly easier while being data-efficient. This robustness was evident even when unconventional choices like emoji pairs were used as labels. BC’s remarkable performance with around 10 unlabeled samples showcases its sample efficiency compared to other methods requiring more than 500 unlabeled samples for stable performance.

The Batch Calibration method is a significant stride towards addressing the challenges associated with the performance of Large Language Models. By successfully mitigating biases associated with design decisions and demonstrating significant performance improvements across various tasks, BC holds promise for more robust and efficient LLM applications in the future.

Former Sequoia Partner Michelle Fradin, Involved in FTX Investment, Joins OpenAI

Michelle Fradin, a former partner at Sequoia Capital known for her key role in the investment in FTX, has transitioned to a new position at OpenAI, where she will spearhead data strategy, acquisitions, and operations. This move marks a significant step in her professional journey, intertwining her expertise in venture capital with the rapidly evolving landscape of artificial intelligence.

At Sequoia Capital, Fradin was integral in shaping investment strategies, particularly in the cryptocurrency sphere, most notably with FTX. Her tenure at Sequoia witnessed a dynamic period, especially in the wake of the FTX collapse, leading to significant shifts within the firm. Beyond her investment acumen, Fradin played a pivotal role in Sequoia’s exploration of AI and its integration into various industries. This experience provided a foundational understanding of the interplay between technology, business, and investment, fueling her transition to a more AI-focused role.

Fradin’s interest in technology and its commercial applications was evident early in her career. Starting at McKinsey, she gained insights into leadership and organizational structures before moving to Google, where she led the Creative Lab team, delving into e-commerce, payments, and AI/ML products. This phase was instrumental in honing her storytelling skills and scouting early-stage investments for Google. Her pursuit of understanding what constitutes a great business led her to Hellman & Friedman, a private equity firm, further solidifying her investment prowess. It was her move to Sequoia that synergized her passion for investing, serving others, and continual learning.

At Sequoia, Fradin was involved in groundbreaking discussions on the role of large language models (LLMs) like ChatGPT in innovation, observing their growing integration into products across various companies. She contributed to Sequoia’s engagement with 33 companies, spanning seed stage startups to large enterprises, to understand their AI strategies and the evolving landscape of AI applications. Her work highlighted the adoption of language model APIs, the importance of retrieval mechanisms for enhancing the quality of AI outputs, and the increasing interest in customizing language models for specific contexts.

Michelle Fradin’s move to OpenAI is a testament to her deep understanding of both the venture capital world and the transformative potential of AI. Her journey from Sequoia Capital to OpenAI reflects a broader trend in the technology sector, where AI is increasingly becoming central to business strategies and operations. As she embarks on this new chapter, her experience and insights are poised to make a significant impact in shaping OpenAI’s data strategies and future innovations.

Virginia Tech Study Reveals Geographic Biases in ChatGPT's Environmental Justice Information

A recent study by researchers at Virginia Tech has brought to light potential geographic biases in ChatGPT, an advanced artificial intelligence (AI) tool. The study, which focused on environmental justice issues, revealed significant variations in ChatGPT’s ability to provide location-specific information across different counties. This finding underscores a critical challenge in the development of AI tools: ensuring equitable access to information regardless of geographic location.

ChatGPT’s Limitations in Smaller, Rural Regions

The research, published in the journal Telematics and Informatics, utilized a comprehensive approach, involving a list of 3,108 counties in the contiguous United States. The researchers asked ChatGPT about environmental justice issues in each of these counties. This methodology revealed that while ChatGPT could effectively provide detailed information for densely populated areas, it struggled in smaller, rural regions. For instance, in states with large urban populations like California or Delaware, less than 1 percent of the population resided in counties where ChatGPT could not offer specific information. Conversely, in more rural states like Idaho and New Hampshire, over 90 percent of the population lived in counties where ChatGPT failed to provide localized information.

Implications and Future Directions

This disparity highlights a crucial limitation of current AI models in addressing the nuanced needs of different geographic locations. Assistant Professor Junghwan Kim, a geographer and geospatial data scientist at Virginia Tech, emphasizes the need for further investigation into these limitations. He points out that recognizing potential biases is essential for future AI development. Assistant Professor Ismini Lourentzou, co-author of the study, suggests refining localized and contextually grounded knowledge in large-language models like ChatGPT. Additionally, she stresses the importance of safeguarding these models against ambiguous scenarios and enhancing user awareness about their strengths and weaknesses.

The study not only identifies the existing geographic biases in ChatGPT but also serves as a call to action for AI developers. Improving the reliability and resiliency of large-language models is imperative, especially in the context of sensitive topics like environmental justice. The findings from Virginia Tech researchers pave the way for more inclusive and equitable AI tools, capable of serving diverse populations with varying needs.

Stanford's WikiChat Addresses Hallucinations Problem and Surpasses GPT-4 in Accuracy

Researchers from Stanford University have unveiled WikiChat, an advanced chatbot system leveraging Wikipedia data to significantly improve the accuracy of responses generated by large language models (LLMs). This innovation addresses the inherent problem of hallucinations – false or inaccurate information – commonly associated with LLMs like GPT-4.

Addressing the Hallucination Challenge in LLMs

LLMs, despite their growing sophistication, often struggle with maintaining factual accuracy, especially in response to recent events or less popular topics. WikiChat, through its integration with Wikipedia, aims to mitigate these limitations. The researchers at Stanford have demonstrated that their approach results in a chatbot that produces almost no hallucinations, marking a significant advancement in the field.

Technical Underpinnings of WikiChat

WikiChat operates on a seven-stage pipeline to ensure the factual accuracy of its responses. These stages include:

Generating queries from Wikipedia data.
Summarizing and filtering the retrieved paragraphs.
Generating responses from an LLM.
Extracting statements from the LLM response.
Fact-checking these statements using the retrieved evidence.
Drafting the response.
Refining the response.

This comprehensive approach not only enhances the factual correctness of responses but also addresses other quality metrics like relevance, informativeness, naturalness, non-repetitiveness, and temporal correctness.

Performance Comparison with GPT-4

In benchmark tests, WikiChat demonstrated a staggering 97.3% factual accuracy, significantly outperforming GPT-4, which scored only 66.1%. This gap was even more pronounced in subsets of knowledge like ‘recent’ and ‘tail’, highlighting the effectiveness of WikiChat in dealing with up-to-date and less mainstream information. Moreover, WikiChat’s optimizations allowed it to outperform state-of-the-art Retrieval-Augmented Generation (RAG) models like Atlas in factual correctness by 8.5%, and in other quality metrics as well.

Potential and Accessibility

WikiChat is compatible with various LLMs and can be accessed via platforms like Azure, openai.com, or Together.ai. It can also be hosted locally, offering flexibility in deployment. For testing and evaluation, the system includes a user simulator and an online demo, making it accessible for broader experimentation and usage.

Conclusion

The emergence of WikiChat marks a significant milestone in the evolution of AI chatbots. By addressing the critical issue of hallucinations in LLMs, Stanford’s WikiChat not only enhances the reliability of AI-driven conversations but also paves the way for more accurate and trustworthy interactions in the digital domain.

Over 70% Accuracy: ChatGPT Shows Promise in Clinical Decision Support

A recent research paper titled “Assessing the Utility of ChatGPT Throughout the Entire Clinical Workflow: Development and Usability Study” published in the Journal of Medical Internet Research evaluates the utility of ChatGPT in clinical decision-making. ChatGPT, a large language model (LLM) based on OpenAI’s Generative Pre-trained Transformer-3.5, was tested using 36 clinical vignettes from the Merck Sharpe & Dohme (MSD) Clinical Manual. The study aimed to assess its performance in providing clinical decision support, encompassing differential diagnoses, diagnostic testing, final diagnosis, and management based on patient demographics and case specifics.

The findings showed that ChatGPT achieved an overall accuracy of 71.7% across all vignettes, excelling in final diagnoses with a 76.9% accuracy rate. However, it had a lower performance in generating initial differential diagnoses, with a 60.3% accuracy rate. The accuracy was consistent across patient age and gender, indicating a broad applicability in various clinical contexts. This performance was measured without ChatGPT’s access to the internet, relying solely on its training data up until 2021.

ChatGPT’s utility was evaluated by presenting each clinical workflow component as a successive prompt, allowing the model to integrate information from earlier parts of the conversation into later responses. This approach mirrors the iterative nature of clinical medicine, where new information continuously updates prior hypotheses.

The study is significant as it presents first-of-its-kind evidence on the potential use of AI tools like ChatGPT throughout the entire clinical workflow. It highlights the model’s ability to adapt and respond to changing clinical scenarios, a crucial aspect of patient care. This research opens new possibilities for AI assistance in healthcare, potentially enhancing decision-making, treatment, and care in various medical settings.

Former Twitter CEO Parag Agrawal's AI Startup Raises $30 Million

Former Twitter CEO Parag Agrawal has marked a significant milestone in his tech career with the successful funding of his new artificial intelligence startup. The venture has raised an impressive $30 million, with the funding round led by Khosla Ventures, a notable investor in the tech world. This financial backing signals strong market confidence in Agrawal’s vision and the potential of his AI startup.

Agrawal’s Shift from Social Media to AI

Agrawal’s journey in the tech industry has been both remarkable and influential. Before stepping into the role of CEO at Twitter, Agrawal held the position of Chief Technology Officer, where he was instrumental in advancing the company’s AI and machine learning initiatives. His tenure as CEO, although brief, was marked by significant developments and challenges, culminating in his departure following Elon Musk’s takeover of Twitter in late 2022.

The New Venture: Focusing on Large Language Models

While specific details about the startup are still under wraps, it is known that the focus will be on developing software for large language model (LLM) developers and their clients. This area of AI technology has gained considerable traction in recent years, primarily driven by the success of models like OpenAI’s ChatGPT. Agrawal’s entry into this field demonstrates a keen understanding of current tech trends and market demands.

The Role of Khosla Ventures and Other Investors

Khosla Ventures, an early supporter of OpenAI, led the $30 million funding round. They were joined by other significant venture firms, including Index Ventures and First Round Capital. The involvement of these firms highlights the potential they see in Agrawal’s startup, particularly in a market increasingly interested in advanced AI solutions.

Agrawal’s Expertise: A Driving Force in the AI Startup

Agrawal’s extensive background in AI and machine learning is a pivotal element in this new venture. His experience at Twitter, coupled with his technical acumen, positions him well to navigate the complexities of developing cutting-edge AI technologies. Agrawal’s move from a leading role in social media to spearheading an AI startup is reflective of the broader shift in the tech industry towards AI-driven innovation.

Prospects and Challenges Ahead

While the initial funding is a significant achievement, Agrawal’s startup faces the challenges of emerging in a highly competitive and rapidly evolving tech landscape. The focus on large language models, a field that has seen exponential growth and interest, places the startup in a promising yet challenging market segment. Success will depend not only on the innovative capabilities of the AI solutions developed but also on effective market positioning and strategic partnerships.

Conclusion

Parag Agrawal’s foray into the AI startup world is a testament to his adaptability and foresight in the tech industry. With a significant $30 million in funding and the backing of renowned venture firms, his startup is poised to make a substantial impact in the field of AI. As the tech world eagerly awaits more details about the startup’s specific products and strategies, Agrawal’s journey from Twitter’s executive suite to leading an AI venture will be closely watched by industry observers and enthusiasts alike.

How LLM Is Reshaping Agent-Based Modeling and Simulation

The groundbreaking integration of Large Language Models (LLMs) into agent-based modeling and simulation is revolutionizing our understanding of complex systems. This integration, detailed in the comprehensive survey “Large Language Models Empowered Agent-based Modeling and Simulation: A Survey and Perspectives,” marks a pivotal advancement in modeling the intricacies of diverse systems and phenomena.

Transformative Role of LLMs in Agent-Based Modeling

A New Dimension to Simulation: Agent-based modeling, focusing on individual agents and their interactions within an environment, has found a powerful ally in LLMs. These models enhance simulations with nuanced decision-making processes, communication abilities, and adaptability within simulated environments.

Critical Abilities of LLMs: LLMs address key challenges in agent-based modeling, such as perception, reasoning, decision-making, and self-evolution. These capabilities significantly elevate the realism and effectiveness of simulations.

Challenges and Approaches in LLM Integration: Constructing LLM-empowered agents for simulation involves overcoming challenges like environment perception, alignment with human knowledge, action selection, and simulation evaluation. Tackling these challenges is crucial for simulations that closely mirror real-world scenarios and human behavior.

Advancements in Various Domains

Social Domain Simulations: LLMs simulate social network dynamics, gender discrimination, nuclear energy debates, and epidemic spread. They also replicate rule-based social environments, such as the Werewolf Game, demonstrating their ability to simulate complex social dynamics.

Simulation of Cooperation: LLM agents collaborate efficiently in tasks like stance detection in social media, structured debates for question-answering, and software development. These simulations demonstrate LLMs’ potential in mimicking human collaborative behaviors.

Future Directions and Open Problems

The survey concludes by discussing open problems and promising future directions in this field. As the area of LLM-empowered agent-based modeling and simulation is new and rapidly evolving, ongoing research and development are expected to uncover more potentials and applications of LLMs in various complex and dynamic systems.

Conclusion

The integration of LLMs into agent-based modeling and simulation represents a significant leap in our ability to model and understand complex, multifaceted systems. This advancement not only enhances our predictive capabilities but also provides invaluable insights into human behavior, societal dynamics, and intricate systems across various domains.

Navigating the Resource Efficiency of Large Language Models: A Comprehensive Survey

The exponential growth of Large Language Models (LLMs) such as OpenAI’s ChatGPT marks a significant advance in AI but raises critical concerns about their extensive resource consumption. This issue is particularly acute in resource-constrained environments like academic labs or smaller tech firms, which struggle to match the computational resources of larger conglomerates. Recently, a research paper titled “Beyond Efficiency: A Systematic Survey of Resource-Efficient Large Language Models” presents a detailed analysis of the challenges and advancements in the field of Large Language Models (LLMs), focusing on their resource efficiency.

The Problem at Hand

LLMs like GPT-3, with billions of parameters, have redefined AI capabilities, yet their size translates into enormous demands for computation, memory, energy, and financial investment. The challenges intensify as these models scale up, creating a resource-intensive landscape that threatens to limit access to advanced AI technologies to only the most well-funded institutions.

Defining Resource-Efficient LLMs

Resource efficiency in LLMs is about achieving the highest performance with the least resource expenditure. This concept extends beyond mere computational efficiency, encapsulating memory, energy, financial, and communication costs. The goal is to develop LLMs that are both high-performing and sustainable, accessible to a wider range of users and applications.

Challenges and Solutions

The survey categorizes the challenges into model-specific, theoretical, systemic, and ethical considerations. It highlights problems like low parallelism in auto-regressive generation, quadratic complexity in self-attention layers, scaling laws, and ethical concerns regarding the transparency and democratization of AI advancements. To tackle these, the survey proposes a range of techniques, from efficient system designs to optimization strategies that balance resource investment and performance gain.

Research Efforts and Gaps

Significant research has been dedicated to developing resource-efficient LLMs, proposing new strategies across various fields. However, there’s a deficiency in systematic standardization and comprehensive summarization frameworks to evaluate these methodologies. The survey identifies this lack of cohesive summary and classification as a significant issue for practitioners who need clear information on current limitations, pitfalls, unresolved questions, and promising directions for future research.

Survey Contributions

This survey presents the first detailed exploration dedicated to resource efficiency in LLMs. Its principal contributions include:

A comprehensive overview of resource-efficient LLM techniques, covering the entire LLM lifecycle.

A systematic categorization and taxonomy of techniques by resource type, simplifying the process of selecting appropriate methods.

Standardization of evaluation metrics and datasets tailored for assessing the resource efficiency of LLMs, facilitating consistent and fair comparisons.

Identification of gaps and future research directions, shedding light on potential avenues for future work in creating resource-efficient LLMs.

Conclusion

As LLMs continue to evolve and grow in complexity, the survey underscores the importance of developing models that are not only technically advanced but also resource-efficient and accessible. This approach is vital for ensuring the sustainable advancement of AI technologies and their democratization across various sectors.

TOFU: How AI Can Forget Your Privacy Data

In the realm of artificial intelligence, the concept of machine learning has been extensively explored and utilized. However, the equally important aspect of machine unlearning has remained largely uncharted. This brings us to TOFU – a Task of Fictitious Unlearning, developed by a team from Carnegie Mellon University. TOFU is a novel project designed to address the challenge of making AI systems “forget” specific data.

Why Unlearning Matters

The increasing capabilities of Large Language Models (LLMs) to store and recall vast amounts of data present significant privacy concerns. LLMs, trained on extensive web corpora, can inadvertently memorize and reproduce sensitive or private data, leading to ethical and legal complications. TOFU emerges as a solution, aiming to selectively erase particular data from AI systems while preserving their overall knowledge base.

The TOFU Dataset

At the heart of TOFU is a unique dataset comprised entirely of fictitious author biographies, synthesized by GPT-4. This data is used to fine-tune LLMs, creating a controlled environment where the only source of information to be unlearned is clearly defined. The TOFU dataset includes diverse profiles, each consisting of 20 question-answer pairs, and a subset known as the “forget set” which serves as the target for unlearning.

Evaluating Unlearning

TOFU introduces a sophisticated evaluation framework to assess unlearning efficacy. This framework includes metrics like Probability, ROUGE scores, and Truth Ratio, applied across diverse datasets – Forget Set, Retain Set, Real Authors, and World Facts. The objective is to fine-tune AI systems to forget the Forget Set while maintaining performance on the Retain Set, ensuring that unlearning is precise and targeted.

Challenges and Future Directions

Despite its innovative approach, TOFU highlights the complexity of machine unlearning. None of the baseline methods evaluated showed effective unlearning, indicating a significant room for improvement in this domain. The intricate balance between forgetting unwanted data and retaining useful information presents a substantial challenge, one that TOFU aims to address in its ongoing development.

Conclusion

TOFU stands as a pioneering effort in the field of AI unlearning. Its approach to handling the sensitive issue of data privacy in LLMs paves the way for future research and development in this crucial area. As AI continues to evolve, projects like TOFU will play a vital role in ensuring that technological advancements align with ethical standards and privacy concerns.

How Jailbreak Attacks Compromise ChatGPT and AI Models' Security

The rapid advancement of artificial intelligence (AI), particularly in the realm of large language models (LLMs) like OpenAI’s GPT-4, has brought with it an emerging threat: jailbreak attacks. These attacks, characterized by prompts designed to bypass ethical and operational safeguards of LLMs, present a growing concern for developers, users, and the broader AI community.

The Nature of Jailbreak Attacks

A paper titled “All in How You Ask for It: Simple Black-Box Method for Jailbreak Attacks” have shed light on the vulnerabilities of large language models (LLMs) to jailbreak attacks. These attacks involve crafting prompts that exploit loopholes in the AI’s programming to elicit unethical or harmful responses. Jailbreak prompts tend to be longer and more complex than regular inputs, often with a higher level of toxicity, to deceive the AI and circumvent its built-in safeguards.

Example of a Loophole Exploitation

The researchers developed a method for jailbreak attacks by iteratively rewriting ethically harmful questions (prompts) into expressions deemed harmless, using the target LLM itself. This approach effectively ‘tricked’ the AI into producing responses that bypassed its ethical safeguards. The method operates on the premise that it’s possible to sample expressions with the same meaning as the original prompt directly from the target LLM. By doing so, these rewritten prompts successfully jailbreak the LLM, demonstrating a significant loophole in the programming of these models.

This method represents a simple yet effective way of exploiting the LLM’s vulnerabilities, bypassing the safeguards that are designed to prevent the generation of harmful content. It underscores the need for ongoing vigilance and continuous improvement in the development of AI systems to ensure they remain robust against such sophisticated attacks.

Recent Discoveries and Developments

A notable advancement in this area was made by researchers Yueqi Xie and colleagues, who developed a self-reminder technique to defend ChatGPT against jailbreak attacks. This method, inspired by psychological self-reminders, encapsulates the user’s query in a system prompt, reminding the AI to adhere to responsible response guidelines. This approach reduced the success rate of jailbreak attacks from 67.21% to 19.34%.

Moreover, Robust Intelligence, in collaboration with Yale University, has identified systematic ways to exploit LLMs using adversarial AI models. These methods have highlighted fundamental weaknesses in LLMs, questioning the effectiveness of existing protective measures.

Broader Implications

The potential harm of jailbreak attacks extends beyond generating objectionable content. As AI systems increasingly integrate into autonomous systems, ensuring their immunity against such attacks becomes vital. The vulnerability of AI systems to these attacks points to a need for stronger, more robust defenses.

The discovery of these vulnerabilities and the development of defense mechanisms have significant implications for the future of AI. They underscore the importance of continuous efforts to enhance AI security and the ethical considerations surrounding the deployment of these advanced technologies.

Conclusion

The evolving landscape of AI, with its transformative capabilities and inherent vulnerabilities, demands a proactive approach to security and ethical considerations. As LLMs become more integrated into various aspects of life and business, understanding and mitigating the risks of jailbreak attacks is crucial for the safe and responsible development and use of AI technologies.