Enhancing LLM Accuracy: Researchers Tackle Unexpected Results with Advanced Techniques
James Khan
Transformation & Business Technology Strategist and Leader, Board & CxO Advisor | University of Oxford Alumnus
Transforming Language Models with Cutting-Edge Reinforcement Learning from Human Feedback!
We're witnessing a groundbreaking era in AI, where Reinforcement Learning (RL) and Reinforcement Learning from Human Feedback (RLHF) are pushing the boundaries of Large Language Models (LLMs). Recent research by Sun, H. [1] and Casper, et al. [3] introduce innovative RL techniques and address potential limitations in RLHF, promising a leap forward in AI capabilities. While Olausson, et al. [5] take a slightly different approach and demonstrate that LINC, a neurosymbolic approach combining Large Language Models with theorem provers for logical reasoning, significantly outperforms existing methods like GPT-3.5 and GPT-4, particularly in complex logical reasoning tasks.
Summary of research by Sun, H.
What Does This Mean for Us (i.e., the end users)?
These advancements are not just technical achievements; they're steps towards an AI-driven future where technology integrates more effectively into our lives, improving our daily experiences.
Infosec Perspective on Potential Misinformation Generation by AI
As my peers working in information security must have guessed it, there are ways to hack the system. Casper, et al., have identified several challenges in Reinforcement Learning from Human Feedback (RLHF) from an infosec perspective. These include difficulties in obtaining quality human feedback, challenges with the reward model (like problem misspecification and reward mis-generalisation), and issues with the policy (such as robust reinforcement learning difficulties and policy mis-generalisation). Fundamental challenges include human limitations in evaluating difficult tasks and the inability of reward models to represent diverse societal values.
Mitigation strategies suggested by Casper, et al., involve improving the RLHF process and its components. For human feedback, solutions include better selection and training of human evaluators, and addressing biases in feedback. In terms of the reward model, maintaining uncertainty and direct human oversight are suggested. For policy challenges, aligning LLMs during pre-training and supervised learning are recommended. Overall, these strategies aim to enhance the reliability, accuracy, and ethical alignment of RLHF processes.
LINC: Logical Inference via Neurosymbolic Computation
Olausson, et al. introduce LINC, a neurosymbolic method combining Large Language Models (LLMs) with theorem provers to enhance logical reasoning. By translating natural language into first-order logic, LINC significantly surpasses the reasoning capabilities of advanced LLMs like GPT-3.5 and GPT-4. This performance is highlighted across various logical reasoning tasks and datasets, demonstrating LINC's effectiveness in complex logical reasoning compared to existing AI models.
领英推荐
LINC's approach, integrating neural and symbolic computing, represents a significant advancement in AI's logical reasoning abilities. It showcases the potential for more accurate and efficient problem-solving capabilities in AI, particularly in tasks requiring deep logical understanding and inference.
For end users, the benefits of LINC are substantial. It provides a more reliable and robust tool for logical reasoning, applicable in fields like law, finance, and scientific research, where accurate logical analysis is critical. This advancement could lead to more sophisticated AI assistants or AI co-pilots, capable of understanding and reasoning through complex problems, thus enhancing decision-making and problem-solving processes in various professional domains.
Acknowledgement: Thanks Dan-George Filimon for bringing this research paper on LINC to attention.
RL vs. RLHF
For reference, the key difference between RL and RLHF is the source of feedback. RL learns from interactions with an environment, while RLHF incorporates feedback from humans to enhance the learning process. RL is often used in scenarios where an AI agent can explore and interact with its environment, while RLHF is valuable when human expertise is needed to provide guidance and evaluation for AI systems, especially in complex tasks like natural language understanding and generation (NLP/NLG).
Bibliography:
[1] Sun, H. (2023) Reinforcement Learning in the Era of LLMs: What is Essential? What is needed? An RL Perspective on RLHF, Prompting, and Beyond, arXiv preprint arXiv:2310.06147. Available from: https://doi.org/10.48550/arXiv.2310.06147 [Accessed 14 January 2024]
[2] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., Schulman, J., Hilton, J., Kelton, F., Miller, L., Simens, M., Askell, A., Welinder, P., Christiano, P., Leike, J., and Lowe, R. (2022) Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, arXiv preprint arXiv:2203.0215. Available from: https://doi.org/10.48550/arXiv.2203.02155 [Accessed 14 January 2024]
[3] Casper, S., Davies, X., Shi, C., Gilbert, T. K., Scheurer, J., Rando, J., Freedman, R., Korbak, T., Lindner, D., Freire, P., Wang, T., Marks, S., Segerie, C.-R., Carroll, M., Peng, A., Christoffersen, P., Damani, M., Slocum, S., Anwar, U., Siththaranjan, A., Nadeau, M., Michaud, E. J., Pfau, J., Krasheninnikov, D., Chen, X., Langosco, L., Hase, P., B?y?k, E., Dragan, A., Krueger, D., Sadigh, D., and Hadfield-Menell, D. (2023) Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback, arXiv preprint arXiv:2307.15217v2. Available from: https://doi.org/10.48550/arXiv.2307.15217 [Accessed 12 January 2024]
[4] Haarnoja, T., Tang, H., Abbeel, P., and Levine, S. (2017) Reinforcement learning with deep energy-based policies. Proceedings of the 34th International Conference on Machine Learning, PMLR 70:1352-1361. Available from: https://proceedings.mlr.press/v70/haarnoja17a.html [Accessed 12 January 2024]
[5] Olausson, T., Gu, A., Lipkin, B., Zhang, C., Solar-Lezama, A., Tenenbaum, J., Levy, R. (2023) LINC: A Neurosymbolic Approach for Logical Reasoning by Combining Language Models with First-Order Logic Provers. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pp. 5153–5176, Association for Computational Linguistics. Available from: https://aclanthology.org/2023.emnlp-main.313.pdf [Accessed 29 January 2024]
#AILanguageModels #ReinforcementLearning #Innovation #TechTrends #FutureOfAI #ArtificialIntelligence #AIHacking #InformationSecurity #NLP #NLG #LINC #NeuroSymbolicComputation #GPT #LLM #TheoremProvers
Transformation & Business Technology Strategist and Leader, Board & CxO Advisor | University of Oxford Alumnus
1 年This is an engaging investigation into a natural degradation phenomenon observed in models. No wonder quality of ChatGPT responses is dropping. Shumailov et al. examine "model collapse," highlighting how models trained on self-generated data progressively lose fidelity to the original data distribution. This issue spans across Gaussian Mixture Models (GMMs), Variational Autoencoders (VAEs), and Large Language Models (LLMs), where reliance on generated content erases nuances from data distribution tails, leading to overly simplified and less diverse outcomes. The study provides theoretical insights into statistical and functional approximation errors as core issues. Through empirical studies across GMMs, VAEs, and LLMs, it demonstrates the tangible effects of model collapse, significantly degrading performance over generations. Incorporating authentic, human-generated data into training sets is proposed as a remedy to maintain model diversity and precision. Research paper: "The Curse of Recursion: Training on Generated Data Makes Models Forget" by Shumailov, I., Shumaylov, Z., Zhao, Y., Gal, Y., Papernot, N., Anderson, R. https://arxiv.org/abs/2305.17493 Credit: Originally shared by Liat Ben-Zur on LinkedIn.
Building delightful narrative experiences for psychological growth
1 年Also cool is this paper by some MIT researchers showing how to generate First-Order-Logic statements with LLMs and use an inference engine to check results. They have some pretty nice results on specific benchmarks, like FOLIO - https://aclanthology.org/2023.emnlp-main.313.pdf
Managing Co-Founder at [ Knosis.ai ] & [ DeepVISS.org ]
1 年Are you maybe familiar with the thought-experiement (inaptly) known-as "The Chinese Room"? https://bogdanbocse.com/2022/05/the-deconstruction-of-the-chinese-room/ ... it is a very useful allegory is we want to carve out the category of limitations of "thinking about language in terms of models" (instead of thinking about them in the wider terms of "tradable particles/symbols/atom of expression and of judgement")