Papers Models Alignment
“Models Alignment” 태그가 달린 논문 21편 · 필터 해제
Into the Unknown: From Structure to Disorder in Protein Function Prediction
Intrinsically disordered regions (IDRs) account for one-third of the human proteome and play essential biological roles. However, predicting the functions of IDRs remains a major challenge due to their lack of stable str…
Models AlignmentProtein Function PredictionLarge Means Left: Political Bias in Large Language Models Increases with Their Number of Parameters
With the increasing prevalence of artificial intelligence, careful evaluation of inherent biases needs to be conducted to form the basis for alleviating the effects these predispositions can have on users. Large language…
MisinformationModels AlignmentAKD : Adversarial Knowledge Distillation For Large Language Models Alignment on Coding tasks
The widespread adoption of Large Language Models (LLMs) for code generation, exemplified by GitHub Copilot\footnote{A coding extension powered by a Code-LLM to assist in code completion tasks} surpassing a million users,…
Code CompletionCode GenerationKnowledge DistillationModels AlignmentStackelberg Game Preference Optimization for Data-Efficient Alignment of Language Models
Aligning language models with human preferences is critical for real-world deployment, but existing methods often require large amounts of high-quality human annotations. Aiming at a data-efficient alignment method, we p…
2kModels AlignmentA Survey of State of the Art Large Vision Language Models: Alignment, Benchmark, Evaluations and Challenges
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 bot…
FairnessHallucinationModels AlignmentVideo Generation+2InfAlign: Inference-aware language model alignment
Language model alignment is a critical step in training modern generative language models. Alignment targets to improve win rate of a sample from the aligned model against the base model. Today, we are increasingly using…
Language ModelingLanguage ModellingmodelModels AlignmentA Survey on LLM-as-a-Judge
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. Large Language Models (LLMs) have achieved…
Models AlignmentSurveyMagnetic Preference Optimization: Achieving Last-iterate Convergence for Language Model Alignment
Self-play methods have demonstrated remarkable success in enhancing model capabilities across various domains. In the context of Reinforcement Learning from Human Feedback (RLHF), self-play not only boosts Large Language…
Language ModelingLanguage ModellingLarge Language ModelModels AlignmentNegative-Prompt-driven Alignment for Generative Language Model
Large language models have achieved remarkable capabilities, but aligning their outputs with human values and preferences remains a significant challenge. Existing alignment methods primarily focus on positive examples w…
Language ModelingLanguage ModellingmodelModels AlignmentVLFeedback: A Large-Scale AI Feedback Dataset for Large Vision-Language Models Alignment
As large vision-language models (LVLMs) evolve rapidly, the demand for high-quality and diverse data to align these models becomes increasingly crucial. However, the creation of such data with human supervision proves co…
DiversityHallucinationModels AlignmentRed Teaming+1VFA: Vision Frequency Analysis of Foundation Models and Human
Machine learning models often struggle with distribution shifts in real-world scenarios, whereas humans exhibit robust adaptation. Models that better align with human perception may achieve higher out-of-distribution gen…
Models AlignmentOut-of-Distribution GeneralizationImproving and Assessing the Fidelity of Large Language Models Alignment to Online Communities
Large language models (LLMs) have shown promise in representing individuals and communities, offering new ways to study complex social dynamics. However, effectively aligning LLMs with specific human groups and systemati…
Models AlignmentComparative Analysis Of Color Models For Human Perception And Visual Color Difference
Color is integral to human experience, influencing emotions, decisions, and perceptions. This paper presents a comparative analysis of various color models' alignment with human visual perception. The study evaluates col…
Models AlignmentOffline Regularised Reinforcement Learning for Large Language Models Alignment
The dominant framework for alignment of large language models (LLM), whether through reinforcement learning from human feedback or direct preference optimisation, is to learn from preference data. This involves building …
DecoderModels Alignmentreinforcement-learningReinforcement Learning+1Decoding-time Realignment of Language Models
Aligning language models with human preferences is crucial for reducing errors and biases in these models. Alignment techniques, such as reinforcement learning from human feedback (RLHF), are typically cast as optimizing…
Models AlignmentConstructive Large Language Models Alignment with Diverse Feedback
In recent research on large language models (LLMs), there has been a growing emphasis on aligning these models with human values to reduce the impact of harmful content. However, current alignment methods often rely sole…
Learning TheoryModels AlignmentQuestion AnsweringText SummarizationTrustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment
Ensuring alignment, which refers to making models behave in accordance with human intentions [1,2], has become a critical task before deploying large language models (LLMs) in real-world applications. For instance, OpenA…
FairnessModels AlignmentCompetence-Based Analysis of Language Models
Despite the recent successes of large, pretrained neural language models (LLMs), comparatively little is known about the representations of linguistic structure they learn during pretraining, which can lead to unexpected…
Models AlignmentRe-basin via implicit Sinkhorn differentiation
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 conn…
Continual LearningIncremental LearningLinear Mode ConnectivityModels Alignment+1Answering Numerical Reasoning Questions in Table-Text Hybrid Contents with Graph-based Encoder and Tree-based Decoder
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 challe…
DecoderModels AlignmentQuestion Answering