Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates.
Foundations of Top-$k$ Decoding for Language Models
Authors: Georgy Noarov, Soham Mallick, Tao Wang, Sunay Joshi, Yan Sun, Yangxinyu Xie, Mengxin Yu, Edgar Dobriban
NeurIPS 2025 | Venue PDF | LLM Run Details | Input Tokens: 47,346 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,474 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, in Section 5, we study the empirical performance of some of the novel decoding schemes on open-ended text generation and mathematical problem solving tasks with LLMs, and find that they perform competitively with top-k decoding. ... We conduct experiments using the GPT-2 Large [43] and Llama 3.1 8B [25] models. We evaluate on two benchmarks: (1) open-ended text generation using the Web Text test set from the GPT-2 output dataset [40], and (2) grade school math reasoning using the GSM8K Chainof-Thought benchmark [13]. |
| Researcher Affiliation | Academia | 1 University of Pennsylvania 2 New Jersey Institute of Technology 3 Washington University in St. Louis |
| Pseudocode | Yes | F.2 Pseudocode for algorithms See Algorithm 3 and Algorithm 4 for pseudocode for sparse primal (resp. dual) Bregman decoding. |
| Open Source Code | Yes | Code is included in the supplementary materials, and an open-source Git Hub repository will be released upon publication. |
| Open Datasets | Yes | We evaluate on two benchmarks: (1) open-ended text generation using the Web Text test set from the GPT-2 output dataset [40], and (2) grade school math reasoning using the GSM8K Chain-of-Thought benchmark [13]. ... The GPT-2 output dataset and GSM8K dataset used are publicly available. |
| Dataset Splits | Yes | We evaluate on two benchmarks: (1) open-ended text generation using the Web Text test set from the GPT-2 output dataset [40], and (2) grade school math reasoning using the GSM8K Chain-of-Thought benchmark [13]. ... For open-ended text generation, following Chen et al. [12], we use the first 35 tokens of each Web Text test sample as a prompt and generate up to 256 tokens. ... Using the full decoding strategy, we evaluate the LLa MA 3.1 8B model using 8-shot Co T prompting. |
| Hardware Specification | Yes | The experiments were conducted on a system running Rocky Linux 8.10, with 64 CPU cores of Intel(R) Xeon(R) Gold 6448Y processors at 2.10 GHz, 1 TB of RAM, and 8 NVIDIA L40S GPUs with 46 GB of memory each. |
| Software Dependencies | Yes | The software environment used Python 3.11.11, Py Torch 2.5.1, and CUDA 12.4. |
| Experiment Setup | Yes | We test various temperatures, regularization strengths λ {0.01, 0.0001} and primal decoding parameters α {1.5, 2.0}. ... Using the full decoding strategy, we evaluate the LLa MA 3.1 8B model using 8-shot Co T prompting. |