Introduction
The Neural Information Processing Systems (NeurIPS) 2023 conference, a premier event in the AI and machine learning field, set new benchmarks in research and collaboration. This year’s conference attracted a record-breaking 13,321 submissions. The rigorous review process, conducted by over 1,100 Area Chairs, 100 senior area chairs, and 396 Ethics reviewers, led to the acceptance of 3,584 papers. This high level of participation underscores the event’s significance as a hub for cutting-edge research and innovation in the AI community.
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Table of contents
- Introduction
- Award Categories
- Outstanding Main Track Papers
- Outstanding Main Track Runner-up Papers
- Outstanding Datasets and Benchmarks Papers
- Bonus: A Legacy of Impact
- Additional Noteworthy Papers
- 7. Tree of Thoughts: Deliberate Problem Solving with Large Language Models
- 8. Toolformer: Language Models Can Teach Themselves to Use Tools
- 9. Zephyr: Direct Distillation of LM Alignment
- 10. Chain of Code: Reasoning with a Language Model-Augmented Code Emulator
- 11. Large Language Models as Zero-Shot Conversational Recommenders
- Conclusion
Award Categories
This year, the awards were categorized into three distinct areas:
- Outstanding Main Track Papers
- Outstanding Main Track Runner-up Papers
- Outstanding Datasets and Benchmarks Papers
Each category honors different aspects of AI research, reflecting the diverse and multifaceted nature of the field.
Outstanding Main Track Papers
1. Privacy Auditing with One (1) Training Run
Authors: Thomas Steinke, Milad Nasr, Matthew Jagielski
Abstract: This groundbreaking paper introduces an innovative method for auditing differentially private machine learning systems using just a single training run. This approach marks a substantial leap from traditional methods that require multiple iterations. The implications are significant, promising advancements in the development of privacy-centric machine learning algorithms, potentially revolutionizing how privacy is maintained in AI.
You can access this paper here.
2. Are Emergent Abilities of Large Language Models a Mirage?
Authors: Rylan Schaeffer, Brando Miranda, Sanmi Koyejo
Abstract: Challenging conventional wisdom, this paper critically examines the supposed emergent abilities of large-scale language models. The authors argue that these abilities may not be inherent to the scaling of AI models but might stem from the metrics used in their evaluation. This provocative stance sparks a reevaluation of our understanding of large language models and underscores the need for more robust metrics to assess AI capabilities accurately.
You can access this paper here.
Outstanding Main Track Runner-up Papers
3. Scaling Data-Constrained Language Models
Authors: Niklas Muennighoff et al.
Abstract: This paper tackles the formidable challenge of scaling language models in scenarios where data is limited. Traditionally, large language models rely on extensive data sets for training. The authors propose innovative techniques to enhance the performance of these models even with smaller data sets, potentially democratizing access to advanced language modeling.
You can access this paper here.
4. Direct Preference Optimization: Your Language Model is Secretly a Reward Model:
Authors: Rafael Rafailov et al.
Abstract: Offering a novel perspective, this paper presents a unique method to control the behavior of large language models by directly optimizing them based on human preferences. This approach could pave the way for creating more user-centric and controllable language models, enhancing their practical usability and ethical alignment.
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Outstanding Datasets and Benchmarks Papers
5. ClimSim: A Large Multi-Scale Dataset for Hybrid Physics-ML Climate Emulation:
Authors: Sungduk Yu et al.
Abstract: This paper introduces ClimSim, an unprecedentedly large dataset tailored for hybrid machine learning and physics research in climate modeling. As the largest dataset of its kind, ClimSim stands to be an invaluable resource for researchers striving to innovate in climate prediction and modeling techniques.
Click here to explore this paper.
6. DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models:
Authors: Boxin Wang et al.
Abstract: This paper addresses a crucial aspect of AI development – trustworthiness. It proposes a comprehensive framework for evaluating the trustworthiness of GPT (Generative Pre-trained Transformer) models, marking a significant step towards developing more reliable and ethically sound language models.
Click here to access this paper.
Bonus: A Legacy of Impact
In keeping with tradition, the conference also featured the “Test of Time” award, presented to a paper from a decade ago that has significantly influenced the field. This year’s recipient was “Distributed Representations of Words and Phrases and their Compositionality” by Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeffrey Dean. First presented at NeurIPS 2013 and now cited over 40,000 times, this paper introduced the groundbreaking word embedding technique, word2vec. Its innovative approach to learning from large volumes of unstructured text spearheaded a new era in natural language processing, marking it as a cornerstone in AI research.
Additional Noteworthy Papers
7. Tree of Thoughts: Deliberate Problem Solving with Large Language Models
Authors: Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L. Griffiths, Yuan Cao, Karthik Narasimhan
Abstract: This paper introduces the “Tree of Thoughts” (ToT) framework for problem solving with language models. It addresses the limitations of language models in tasks requiring exploration, strategic lookahead, or pivotal initial decisions. ToT enables exploration over coherent units of text (“thoughts”) as intermediate steps towards problem solving. It allows language models to perform deliberate decision-making by considering multiple reasoning paths, self-evaluating choices, and looking ahead or backtracking as necessary. The framework significantly enhances problem-solving abilities in tasks requiring non-trivial planning or search, as demonstrated in experiments like the Game of 24, Creative Writing, and Mini Crosswords.
Click here to access this paper.
8. Toolformer: Language Models Can Teach Themselves to Use Tools
Authors: Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, Roberta Raileanu, Maria Lomeli, Luke Zettlemoyer, Nicola Cancedda, Thomas Scialom
Abstract: In this paper, the authors present Toolformer, a language model (LM) designed to leverage external tools through simple APIs, addressing the paradox where LMs excel in learning new tasks but struggle with basic functions like arithmetic or factual lookup. Toolformer is trained to autonomously determine when and how to use these tools, including a calculator, Q&A system, search engine, translation system, and calendar. It does so in a self-supervised manner with minimal demonstrations. This approach significantly enhances zero-shot performance across various tasks and maintains the model’s core language abilities.
Click here to explore this paper.
9. Zephyr: Direct Distillation of LM Alignment
Authors: Lewis Tunstall et al.
Abstract: This paper introduces ZEPHYR-7B, a 7-billion parameter language model designed for improved alignment with user intent. It employs distilled supervised fine-tuning (dSFT) and distilled direct preference optimization (dDPO) with AI Feedback (AIF) from outputs ranked by a teacher model. This efficient approach requires only a few hours of training and no extra sampling during fine-tuning. ZEPHYR-7B demonstrates superior performance on chat benchmarks compared to existing models, including the LLAMA2-CHAT-70B, without the need for human annotation. The resources related to this system are shared online for public access.
Click here to explore this paper.
10. Chain of Code: Reasoning with a Language Model-Augmented Code Emulator
Authors: Chengshu Li, Jacky Liang, Andy Zeng, Xinyun Chen, Karol Hausman, Dorsa Sadigh, Sergey Levine, Li Fei-Fei, Fei Xia, Brian Ichter
Abstract: This paper introduces Chain of Code (CoC), an extension to enhance language models’ (LMs) reasoning abilities, particularly in tasks requiring a mix of logic, arithmetic, and semantic understanding. CoC encourages LMs to format semantic sub-tasks as flexible pseudocode, allowing an “LMulator” to handle undefined behaviors. This approach outperforms Chain of Thought and other baselines in various benchmarks. Notably, CoC achieves a 12% gain over Chain of Thought in BIG-Bench Hard, demonstrating its effectiveness in expanding the range of reasoning questions LMs can accurately address by using code-driven thinking.
Click here to explore this paper.
11. Large Language Models as Zero-Shot Conversational Recommenders
Authors: Zhankui He et al.
Abstract: This paper presents an empirical study on conversational recommendation using large language models in a zero-shot setting. It includes three main contributions: the creation of the largest public real-world conversational recommendation dataset from a popular discussion website, an evaluation showing that large language models outperform existing fine-tuned models even without fine-tuning, and an analysis of the models’ performance through probing tasks. This analysis helps understand the effectiveness and limitations of large language models in conversational recommendation, offering directions for future design.
Click here to explore this paper.
Conclusion
NeurIPS 2023 exemplified the vibrant and rapidly evolving landscape of AI and machine learning research. The record number of submissions and the rigorous review process highlighted the event’s prominence as a nexus for innovative research. The diverse array of award categories celebrated achievements across various facets of AI, from groundbreaking methods in privacy auditing and challenges to conventional beliefs about emergent abilities in large language models, to novel approaches in scaling language models with limited data and assessing the trustworthiness of AI systems.
The introduction of significant datasets like ClimSim further underlines the conference’s role in fostering advancements across interdisciplinary fields. The “Test of Time” award, recognizing the enduring impact of the word2vec paper, served as a reminder of the lasting influence of pioneering research. Additionally, intriguing papers like “Tree of Thoughts” and “Toolformer” demonstrated the continuous push towards more sophisticated and practical applications of AI, revealing a future where language models not only understand but also interact with the world in increasingly complex ways.
NeurIPS 2023 was not just a showcase of current achievements but also a beacon for future explorations, setting the stage for continued innovation and discovery in the AI community.
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