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Anant Khandelwal

4 accepted papers

2026

Linguistic Properties and Model Scale in Brain Encoding: From Small to Compressed Language Models

ICML 2026spotlight

Recent work has shown that scaling large language models (LLMs) improves their alignment with human brain activity, yet it remains unclear what drives these gains or which representational properties are responsible. Although larger models often yield better task performance and brain alignment, the…

Cited by 0SourceScholar
2025

CoCoA: Confidence- and Context-Aware Adaptive Decoding for Resolving Knowledge Conflicts in Large Language Models

EMNLP 2025

Faithful generation in large language models (LLMs) is challenged by knowledge conflicts between parametric memory and external context. Existing contrastive decoding methods tuned specifically to handle conflict often lack adaptability and can degrade performance in low conflict settings. We introd

Cited by 0SourcePDFScholar
2023

Large Scale Generative Multimodal Attribute Extraction for E-commerce Attributes

ACL 2023industry

E-commerce websites (e.g. Amazon, Alibaba) have a plethora of structured and unstructured information (text and images) present on the product pages. Sellers often don’t label or mislabel values of the attributes (e.g. color, size etc.) for their products. Automatically identifying these attribute v…

Cited by 10SourcePDFScholar