AI Transparency in the Age of LLMs: A Human-Centered Research Roadmap

• URL: http://arxiv.org/abs/2306.01941v2
• Date: Tue, 8 Aug 2023 01:41:22 GMT
• Title: AI Transparency in the Age of LLMs: A Human-Centered Research Roadmap
• Authors: Q. Vera Liao and Jennifer Wortman Vaughan
• Abstract summary: The rise of powerful large language models (LLMs) brings about tremendous opportunities for innovation but also looming risks for individuals and society at large. We have reached a pivotal moment for ensuring that LLMs and LLM-infused applications are developed and deployed responsibly. It is paramount to pursue new approaches to provide transparency for LLMs.
• Score: 46.98582021477066
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rise of powerful large language models (LLMs) brings about tremendous opportunities for innovation but also looming risks for individuals and society at large. We have reached a pivotal moment for ensuring that LLMs and LLM-infused applications are developed and deployed responsibly. However, a central pillar of responsible AI -- transparency -- is largely missing from the current discourse around LLMs. It is paramount to pursue new approaches to provide transparency for LLMs, and years of research at the intersection of AI and human-computer interaction (HCI) highlight that we must do so with a human-centered perspective: Transparency is fundamentally about supporting appropriate human understanding, and this understanding is sought by different stakeholders with different goals in different contexts. In this new era of LLMs, we must develop and design approaches to transparency by considering the needs of stakeholders in the emerging LLM ecosystem, the novel types of LLM-infused applications being built, and the new usage patterns and challenges around LLMs, all while building on lessons learned about how people process, interact with, and make use of information. We reflect on the unique challenges that arise in providing transparency for LLMs, along with lessons learned from HCI and responsible AI research that has taken a human-centered perspective on AI transparency. We then lay out four common approaches that the community has taken to achieve transparency -- model reporting, publishing evaluation results, providing explanations, and communicating uncertainty -- and call out open questions around how these approaches may or may not be applied to LLMs. We hope this provides a starting point for discussion and a useful roadmap for future research.

Related papers

• A Reality check of the benefits of LLM in business [1.9181612035055007]
  Large language models (LLMs) have achieved remarkable performance in language understanding and generation tasks. This paper thoroughly examines the usefulness and readiness of LLMs for business processes.
  arXiv  Detail & Related papers  (2024-06-09T02:36:00Z)
• Federated TrustChain: Blockchain-Enhanced LLM Training and Unlearning [22.33179965773829]
  We propose a novel blockchain-based federated learning framework for Large Language Models (LLMs) Our framework leverages blockchain technology to create a tamper-proof record of each model's contributions and introduces an innovative unlearning function that seamlessly integrates with the federated learning mechanism.
  arXiv  Detail & Related papers  (2024-06-06T13:44:44Z)
• Navigating LLM Ethics: Advancements, Challenges, and Future Directions [5.023563968303034]
  This study addresses ethical issues surrounding Large Language Models (LLMs) within the field of artificial intelligence. It explores the common ethical challenges posed by both LLMs and other AI systems. It highlights challenges such as hallucination, verifiable accountability, and decoding censorship complexity.
  arXiv  Detail & Related papers  (2024-05-14T15:03:05Z)
• A Survey on Self-Evolution of Large Language Models [116.54238664264928]
  Large language models (LLMs) have significantly advanced in various fields and intelligent agent applications. To address this issue, self-evolution approaches that enable LLMs to autonomously acquire, refine, and learn from experiences generated by the model itself are rapidly growing.
  arXiv  Detail & Related papers  (2024-04-22T17:43:23Z)
• Small Models, Big Insights: Leveraging Slim Proxy Models To Decide When and What to Retrieve for LLMs [60.40396361115776]
  This paper introduces a novel collaborative approach, namely SlimPLM, that detects missing knowledge in large language models (LLMs) with a slim proxy model. We employ a proxy model which has far fewer parameters, and take its answers as answers. Heuristic answers are then utilized to predict the knowledge required to answer the user question, as well as the known and unknown knowledge within the LLM.
  arXiv  Detail & Related papers  (2024-02-19T11:11:08Z)
• Rethinking Machine Unlearning for Large Language Models [85.92660644100582]
  We explore machine unlearning in the domain of large language models (LLMs) This initiative aims to eliminate undesirable data influence (e.g., sensitive or illegal information) and the associated model capabilities.
  arXiv  Detail & Related papers  (2024-02-13T20:51:58Z)
• Rethinking Interpretability in the Era of Large Language Models [76.1947554386879]
  Large language models (LLMs) have demonstrated remarkable capabilities across a wide array of tasks. The capability to explain in natural language allows LLMs to expand the scale and complexity of patterns that can be given to a human. These new capabilities raise new challenges, such as hallucinated explanations and immense computational costs.
  arXiv  Detail & Related papers  (2024-01-30T17:38:54Z)
• RECALL: A Benchmark for LLMs Robustness against External Counterfactual Knowledge [69.79676144482792]
  This study aims to evaluate the ability of LLMs to distinguish reliable information from external knowledge. Our benchmark consists of two tasks, Question Answering and Text Generation, and for each task, we provide models with a context containing counterfactual information.
  arXiv  Detail & Related papers  (2023-11-14T13:24:19Z)
• Challenges and Contributing Factors in the Utilization of Large Language Models (LLMs) [10.039589841455136]
  This review explores the issue of domain specificity, where large language models (LLMs) may struggle to provide precise answers to specialized questions within niche fields. It's suggested to diversify training data, fine-tune models, enhance transparency and interpretability, and incorporate ethics and fairness training.
  arXiv  Detail & Related papers  (2023-10-20T08:13:36Z)
• Deception Abilities Emerged in Large Language Models [0.0]
  Large language models (LLMs) are currently at the forefront of intertwining artificial intelligence (AI) systems with human communication and everyday life. This study reveals that such strategies emerged in state-of-the-art LLMs, such as GPT-4, but were non-existent in earlier LLMs. We conduct a series of experiments showing that state-of-the-art LLMs are able to understand and induce false beliefs in other agents.
  arXiv  Detail & Related papers  (2023-07-31T09:27:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.