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Anirudh Phukan

4 accepted papers

2025

Beyond Logit Lens: Contextual Embeddings for Robust Hallucination Detection & Grounding in VLMs

NAACL 2025long

The rapid development of Large Multimodal Models (LMMs) has significantly advanced multimodal understanding by harnessing the language abilities of Large Language Models (LLMs) and integrating modality-specific encoders. However, LMMs are plagued by hallucinations that limit their reliability and ad…

Cited by 1SourcePDFScholar
2025

PLD+: Accelerating LLM Inference by Leveraging Language Model Artifacts

NAACL 2025findings

To reduce the latency associated with autoretrogressive LLM inference, speculative decoding has emerged as a novel decoding paradigm, where future tokens are drafted and verified in parallel. However, the practical deployment of speculative decoding is hindered by its requirements for additional com…

Cited by 1SourcePDFScholar
2024

IPCL: Iterative Pseudo-Supervised Contrastive Learning to Improve Self-Supervised Feature Representation

ICASSP 2024accepted

Self-supervised learning with a contrastive batch approach has become a powerful tool for representation learning in computer vision. The performance of downstream tasks is proportional to the quality of visual features learned while self-supervised pre-training. The existing contrastive batch appro…

Cited by 0SourceScholar
2024

Peering into the Mind of Language Models: An Approach for Attribution in Contextual Question Answering

ACL 2024findings

With the enhancement in the field of generative artificial intelligence (AI), contextual question answering has become extremely relevant. Attributing model generations to the input source document is essential to ensure trustworthiness and reliability. We observe that when large language models (LL…