The Responsible AI Bulletin #26: Guide to LLMs, GenAI to help research writing, and deliberation with LLMs.

Welcome to this edition of The?Responsible AI Bulletin, a weekly agglomeration of?research developments?in the field from around the Internet that caught my attention - a few morsels to dazzle in your next discussion on AI, its ethical implications, and what it means for?our future.

For those looking for more detailed investigations into research and reporting in the field of Responsible AI, I recommend subscribing to the AI Ethics Brief, published by my team at the Montreal AI Ethics Institute, an international non-profit research institute with a mission to democratize AI ethics literacy.

Language Models: A Guide for the Perplexed

Given the growing importance of AI literacy, we decided to write this tutorial on language models (LMs) to help narrow the gap between the discourse among those who study LMs—the core technology underlying ChatGPT and similar products—and those who are intrigued and want to learn more about them. In short, we believe the perspective of researchers and educators can clarify the public’s understanding of the technologies beyond what’s currently available, which tends to be either extremely technical or promotional material generated about products by their purveyors.

Our approach teases apart the concept of a language model (LM) from products built on them, from the behaviors attributed to or desired from those products, and claims about similarity to human cognition. As a starting point, we (1) offer a scientific viewpoint that focuses on questions amenable to study through experimentation; (2) situate language models as they are today in the context of the research that led to their development; and (3) describe the boundaries of what is known about the models at this writing.

Continue reading here.

Generative AI in Writing Research Papers: A New Type of Algorithmic Bias and Uncertainty in Scholarly Work

Have you ever found LLM-based tools catering their answers to you based on the information you provide them? What about catering to demographic information you inadvertently leave in your prompt?

Our research paper “Generative AI in Writing Research Papers: A New Type of Algorithmic Bias and Uncertainty in Scholarly Work” delves into the consequences of employing large language models (LLMs) like ChatGPT in academic writing. We scrutinize the biases introduced by these generative AI tools, which stem from their training on vast, diverse datasets combined with human feedback.?

Such biases are harder to detect and address, potentially leading to issues like goal misgeneralization, hallucinations, and susceptibility to adversarial attacks. This study conducts a systematic review to quantify the influence of generative AI in academic authorship and highlights the emerging types of biases, particularly context-induced biases.?

Incorporating generative AI in academic writing introduces unique challenges and biases, adversely impacting the integrity and development of scholarly work.

Continue reading here.

LLM-Deliberation: Evaluating LLMs with Interactive Multi-Agent Negotiation Games

We know that LLMs have been primarily trained unsupervised on massive datasets. Despite that, they perform relatively well in setups beyond traditional NLP tasks, such as using tools. LLMs are now used in many real-world applications, and there is an increasing interest in using them as interactive autonomous agents. Given this discrepancy between training paradigms and these new adoptions, how can we build new evaluation frameworks that better match real-world use cases and help us understand and systematically test models’ capabilities, limitations, and potential misuse?

To answer this, we first look for a task that is highly needed. Previous work on reinforcement learning (RL) agents told us to look no more; the answer is negotiation!

Negotiation is a key part of human communication. We use one form or another of negotiation tactics to, e.g., schedule meetings, make a new contract with our phone service provider, or agree on a salary and compensation for a new job. It is also the base of high-stakes decisions such as peace mediation or agreeing on loans.???

The second motivation behind using motivation is that it entails many other important sub-tasks. Agents must assess the value of deals according to their own goals, have a representation of others’ goals, update this representation based on newer observations, plan and adapt their strategies over rounds, weigh different options, and finally find common grounds. This requires non-trivial arithmetic and reasoning skills, including Theory-of-Mind (ToM) abilities.?

We first leverage an existing commonly-used scorable role-play negotiation game with multi-party and multi-issue negotiation. To rule out memorization and provide a rich benchmark, we create semantically equivalent games by perturbing the names of parties/issues, and we use an LLM as a seed to design three completely new and diverse games. The difficulty of these games can be easily tuned, creating a less saturating benchmark.

While we found that GPT-4 performs significantly better than earlier models, our analysis goes deeper than that. We will soon have a network of agents, each representing an entity, and they are autonomously communicating to agree on plans. Or, at the very least, it is easy to imagine a company utilizing an LLM in a customer service chatbot; in this scenario, can it be influenced to offer deals that are not in the company’s best interest? To answer this, we study agents’ interaction in unbalanced adversarial settings. We show that agents’ behavior can be modulated to promote greediness or attack other agents, frequently sabotaging the negotiation and altering other cooperative agents’ behaviors as well.?

Continue reading here.

Comment and let me know?what you liked and if you have any recommendations on what I should read and cover next week. You can learn more about my work here. See you soon!