Papers with context grounding

3 papers
Enhancing Contextual Understanding in Large Language Models through Contrastive Decoding (2024.naacl-long)

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Challenge: Large language models lack contextual knowledge, resulting in text with factual inconsistencies or contextually unfaithful content.
Approach: They propose a method that integrates contrastive decoding with adversarial irrelevant passages as negative samples to enhance robust context grounding during generation.
Outcome: The proposed method improves context grounding during generation without training.
Understanding and Leveraging the Expert Specialization of Context Faithfulness in Mixture-of-Experts LLMs (2025.emnlp-main)

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Challenge: Context faithfulness is essential for reliable reasoning in context-dependent scenarios.
Approach: They propose a method that identifies and fine-tunes context-faithful experts . they propose 'context-faither fine- tuning' which selectively fine- tunes them .
Outcome: The proposed method identifies experts with specialization in context utilization and improves context grounding.
Towards Dog Bark Decoding: Leveraging Human Speech Processing for Automated Bark Classification (2024.lrec-main)

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Challenge: Similar to humans, animals make extensive use of verbal and non-verbal forms of communication, including audio signals.
Approach: They propose to use self-supervised speech representation models pre-trained on human speech to address dog bark classification tasks.
Outcome: The proposed model improves dog recognition, breed identification, gender classification, and context grounding tasks.

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