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Models Alignment

6 papers with code · 0 benchmarks · 1 dataset archive 2025-07-28

Models Alignment is the process of ensuring that multiple models used in a machine learning system are consistent with each other and aligned with the goals of the system. This involves defining clear and consistent objectives for each model, identifying and addressing any inconsistencies or biases in the data used to train each model, testing and validating each model to ensure its accuracy, and ensuring that the predictions and decisions made by each model are consistent and aligned with the overall goals of the system.

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

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Libraries

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Datasets archive 2025-07-28

1 dataset whose archive record lists this task, ordered by the archive's paper count.

ListUltraFeedback

Subtasks archive 2025-07-28

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Most implemented papers archive 2025-07-28

6 shown of 6 papers with code (21 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

A Survey of State of the Art Large Vision Language Models: Alignment, Benchmark, Evaluations and Challenges

4 Jan 2025 3 repositories listed

Multimodal Vision Language Models (VLMs) have emerged as a transformative topic at the intersection of computer vision and natural language processing, enabling machines to perceive and reason about the world through…
A Survey on LLM-as-a-Judge

23 Nov 2024 2 repositories listed

Accurate and consistent evaluation is crucial for decision-making across numerous fields, yet it remains a challenging task due to inherent subjectivity, variability, and scale.
VFA: Vision Frequency Analysis of Foundation Models and Human

9 Sep 2024 1 repository listed

Machine learning models often struggle with distribution shifts in real-world scenarios, whereas humans exhibit robust adaptation.
Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment

10 Aug 2023 1 repository listed

However, a major challenge faced by practitioners is the lack of clear guidance on evaluating whether LLM outputs align with social norms, values, and regulations.
Re-basin via implicit Sinkhorn differentiation

22 Dec 2022 1 repository listed Syntology ran 1 of 1 samples · 0 unverified

The recent emergence of new algorithms for permuting models into functionally equivalent regions of the solution space has shed some light on the complexity of error surfaces, and some promising properties like mode…
Answering Numerical Reasoning Questions in Table-Text Hybrid Contents with Graph-based Encoder and Tree-based Decoder

16 Sep 2022 1 repository listed

In the real-world question answering scenarios, hybrid form combining both tabular and textual contents has attracted more and more attention, among which numerical reasoning problem is one of the most typical and…

Syntology lines on 1 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.

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