Fetching the paper…
Reading the bibliography…
While large language models (LLMs) have shown exceptional capabilities in understanding complex queries and performing sophisticated tasks, their generalization abilities are often deeply entangled with memorization, necessitating more precise evaluation.
Introduction to Algorithms
Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein · 2009
Earlier work this paper cites.
Measuring massive multitask language understanding
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt · 2020
Earlier work this paper cites.
Scaling laws for neural language models
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei · 2020
Earlier work this paper cites.
Evaluating large language models trained on code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde De Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al · 2021
Earlier work this paper cites.
Training verifiers to solve math word problems
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, et al · 2021
Earlier work this paper cites.
Measuring mathematical problem solving with the math dataset
Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and Jacob Steinhardt · 2021
Earlier work this paper cites.
Memorisation versus generalisation in pre-trained language models
Michael Tänzer, Sebastian Ruder, and Marek Rei · 2021
Earlier work this paper cites.
Understanding deep learning (still) requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2021
Earlier work this paper cites.
Exploring length generalization in large language models
Cem Anil, Yuhuai Wu, Anders Andreassen, Aitor Lewkowycz, Vedant Misra, Vinay Ramasesh, Ambrose Slone, Guy Gur-Ari, Ethan Dyer, and Behnam Neyshabur · 2022
Earlier work this paper cites.
Quantifying memorization across neural language models
Nicholas Carlini, Daphne Ippolito, Matthew Jagielski, Katherine Lee, Florian Tramer, and Chiyuan Zhang · 2022
Earlier work this paper cites.
Folio: Natural language reasoning with first-order logic
Simeng Han, Hailey Schoelkopf, Yilun Zhao, Zhenting Qi, Martin Riddell, Wenfei Zhou, James Coady, David Peng, Yujie Qiao, Luke Benson, et al · 2022
Earlier work this paper cites.
Training compute-optimal large language models
Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, et al · 2022
Earlier work this paper cites.
Large language models are zero-shot reasoners
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa · 2022
Earlier work this paper cites.
Impact of pretraining term frequencies on few-shot reasoning
Yasaman Razeghi, Robert L Logan IV, Matt Gardner, and Sameer Singh · 2022
Earlier work this paper cites.
Beyond the imitation game: Quantifying and extrapolating the capabilities of language models
Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, Abu Awal Md Shoeb, Abubakar Abid, Adam Fisch, Adam R Brown, Adam Santoro, Aditya Gupta, Adrià Garriga-Alonso, et al · 2022
Earlier work this paper cites.
Challenging big-bench tasks and whether chain-of-thought can solve them
Mirac Suzgun, Nathan Scales, Nathanael Schärli, Sebastian Gehrmann, Yi Tay, Hyung Won Chung, Aakanksha Chowdhery, Quoc V Le, Ed H Chi, Denny Zhou, , and Jason Wei · 2022
Cited alongside, same era.
The clrs algorithmic reasoning benchmark
Petar Veličković, Adrià Puigdomènech Badia, David Budden, Razvan Pascanu, Andrea Banino, Misha Dashevskiy, Raia Hadsell, and Charles Blundell · 2022
Cited alongside, same era.
Sparks of artificial general intelligence: Early experiments with gpt-4
Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, et al · 2023
Cited alongside, same era.
Nphardeval: Dynamic benchmark on reasoning ability of large language models via complexity classes
Lizhou Fan, Wenyue Hua, Lingyao Li, Haoyang Ling, Yongfeng Zhang, and Libby Hemphill · 2023
Cited alongside, same era.
Emergent and predictable memorization in large language models
Stella Biderman, Usvsn Prashanth, Lintang Sutawika, Hailey Schoelkopf, Quentin Anthony, Shivanshu Purohit, and Edward Raff · 2024
Closest in time.
Yihong Dong, Xue Jiang, Huanyu Liu, Zhi Jin, and Ge Li · 2024
Closest in time.
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
Google · 2024
Closest in time.
Scaling laws for downstream task performance of large language models
Berivan Isik, Natalia Ponomareva, Hussein Hazimeh, Dimitris Paparas, Sergei Vassilvitskii, and Sanmi Koyejo · 2024
Closest in time.
Can large language models reason and plan?
Subbarao Kambhampati · 2024
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Shahriar Golchin and Mihai Surdeanu · 2023
Cited alongside, same era.
Large language models struggle to learn long-tail knowledge
Nikhil Kandpal, Haikang Deng, Adam Roberts, Eric Wallace, and Colin Raffel · 2023
Cited alongside, same era.
Boardgameqa: A dataset for natural language reasoning with contradictory information
Mehran Kazemi, Quan Yuan, Deepti Bhatia, Najoung Kim, Xin Xu, Vaiva Imbrasaite, and Deepak Ramachandran · 2023
Cited alongside, same era.
Non-vacuous generalization bounds for large language models
Sanae Lotfi, Marc Finzi, Yilun Kuang, Tim GJ Rudner, Micah Goldblum, and Andrew Gordon Wilson · 2023
Cited alongside, same era.
OpenAI · 2023
Cited alongside, same era.
Large language models are in-context semantic reasoners rather than symbolic reasoners
Xiaojuan Tang, Zilong Zheng, Jiaqi Li, Fanxu Meng, Song-Chun Zhu, Yitao Liang, and Muhan Zhang · 2023
Cited alongside, same era.
Zhaofeng Wu, Linlu Qiu, Alexis Ross, Ekin Akyürek, Boyuan Chen, Bailin Wang, Najoung Kim, Jacob Andreas, and Yoon Kim · 2023
Cited alongside, same era.
Causal parrots: Large language models may talk causality but are not causal
Matej Zečević, Moritz Willig, Devendra Singh Dhami, and Kristian Kersting · 2023
Cited alongside, same era.
The clrs-text algorithmic reasoning language benchmark
Larisa Markeeva, Sean McLeish, Borja Ibarz, Wilfried Bounsi, Olga Kozlova, Alex Vitvitskyi, Charles Blundell, Tom Goldstein, Avi Schwarzschild, and Petar Veličković · 2024
Closest in time.
Learning to reason with llms
OpenAI · 2024
Closest in time.
Mutual reasoning makes smaller llms stronger problem-solvers
Zhenting Qi, Mingyuan Ma, Jiahang Xu, Li Lyna Zhang, Fan Yang, and Mao Yang · 2024
Closest in time.
Rethinking llm memorization through the lens of adversarial compression
Avi Schwarzschild, Zhili Feng, Pratyush Maini, Zachary C Lipton, and J Zico Kolter · 2024
Closest in time.
Scaling llm test-time compute optimally can be more effective than scaling model parameters
Charlie Snell, Jaehoon Lee, Kelvin Xu, and Aviral Kumar · 2024
Closest in time.
Functional benchmarks for robust evaluation of reasoning performance, and the reasoning gap
Saurabh Srivastava, Anto PV, Shashank Menon, Ajay Sukumar, Alan Philipose, Stevin Prince, Sooraj Thomas, et al · 2024
Closest in time.
Q*: Improving multi-step reasoning for llms with deliberative planning
Chaojie Wang, Yanchen Deng, Zhiyi Lv, Shuicheng Yan, and An Bo · 2024
Closest in time.
Livebench: A challenging, contamination-free llm benchmark
Colin White, Samuel Dooley, Manley Roberts, Arka Pal, Ben Feuer, Siddhartha Jain, Ravid Shwartz-Ziv, Neel Jain, Khalid Saifullah, Siddartha Naidu, et al · 2024
Closest in time.
Transformers can achieve length generalization but not robustly
Yongchao Zhou, Uri Alon, Xinyun Chen, Xuezhi Wang, Rishabh Agarwal, and Denny Zhou · 2024
Closest in time.