The DOI matches its Crossref record.
Decoding-Time Language Model Alignment with Multiple Objectives
Overview
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Registry
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Claims the paper makes
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Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“The method demonstrates significant effectiveness when compared to the parametermerging baseline.”
From A survey of personalized large language models: progress and future directions (2025) · cites this paper as Shi et al., 2024
In the citing paper
“Also, the personalized fusion of policy LLMs can be achieved not only through parameter merging but also through model ensembling.”
From A survey of personalized large language models: progress and future directions (2025) · cites this paper as Shi et al., 2024
In the citing paper
“MOD [Shi et al., 2024] outputs the next token from a linear combination of all base models, allowing for precise control over different objectives by combining their predictions without the need for retraining.”
From A survey of personalized large language models: progress and future directions (2025) · cites this paper as Shi et al., 2024
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
A survey of personalized large language models: progress and future directions
Liu, J., Qiu, Z., Li, Z., Dai, Q., Yu, W., Zhu, J., Hu, M., Yang, M., Chua, T.-S., & King, I. (2025) · arXiv · doi:10.48550/arxiv.2502.11528 · Cited by 146
Metadata
- Authors
- Shi, R., Chen, Y., Hu, Y., Liu, A., Hajishirzi, H., Smith, N., & Du, S.
- Venue
- Advances in Neural Information Processing Systems 37
- Published
- 2024
- Pages
- 48875-48920
- Cited by
- 6
- References
- 0
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
