Author

Anil Ramakrishna

Amazon

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Preprint Aug 2026

From Interpretability to Control: Insights from Six Years of the TrustNLP Workshop

Analysis from the classification study reveals that truthfulness is the fastest-growing dimension, fairness remains the most consistent theme, and explainability exhibits a U-shaped trajectory; declining as post-hoc methods lost relevance but resurging in 2026 through mechanistic interpretability.

Rahul Gupta, Abhinav Mohanty, Anaelia Ovalle et al. · 0 citations

Jul 2026

Tokenizing Numerical and Embedding Features for LLM RecSys

Experiments on three Amazon recommendation benchmarks show that soft-token fusion improves retrieval performance over LLM-based baselines, and that interaction-based fusion is more effective than direct concatenation of heterogeneous soft tokens.

Zhe Xu, Ankit Peshin, Chiyu Zhang et al. · arXiv.org · 0 citations

Jul 2026

The Case Against Generation for Retrieval: Discriminative Language Models as Effective Retrievers

Large Language Models (LLMs) have emerged as powerful assets for recommender systems. However, deploying them as generative recommenders or zero-shot rankers at web-scale remains bottlenecked by prohibitive computational overhead and grounding challenges. In this paper, we revitalize the classic, highly efficient two-t...

Zhe Xu, Prachi Agrawal, Kavosh Asadi et al. · arXiv.org · 0 citations