most citedMASCA: LLM based-Multi Agents System for Credit Assessment

1 citations · 1 across the 1 of their papers we have counts for

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8 papers

cs.CL20261 cited

MASCA: LLM based-Multi Agents System for Credit Assessment

Gautam Jajoo, Atharva Pandey, Pranjal A Chitale +1

Recent advancements in financial problem-solving have leveraged LLMs and agent-based systems, with a primary focus on trading and financial modeling. However, credit assessment rem…

cs.CL2026

DEPART: DEcomposing PARiTy across Multilingual LLMs

Manan Uppadhyay, Prashant Kodali, Pranjal Chitale +3

Multilingual Large Language Models (mLLMs) leaderboards report per-language accuracy but rarely explain why disparities emerge, leaving systemic biases unattributed and offering pr…

cs.IR2026

HARNESS-LM: A Three-Phase Training Recipe for Harnessing SLMs in Sponsored Search Retrieval

Vipul Gupta, Shikhar Mohan, Lakshya Kumar +4

In the competitive landscape of sponsored search, balancing retrieval quality with production latency is a critical challenge. While large retrieval models based on Small Language…

cs.IR2026

HORIZON: A Benchmark for In-the-wild User Behaviour Modeling

Arnav Goel, Pranjal A Chitale, Bhawna Paliwal +2

User behavior in the real world is diverse, cross-domain, and spans long time horizons. Existing user modeling benchmarks however remain narrow, focusing mainly on short sessions a…

cs.CL2026

UPDESH: Synthesizing Grounded Instruction Tuning Data for 13 Indic Languages

Pranjal A. Chitale, Varun Gumma, Sanchit Ahuja +4

Developing culturally grounded multilingual AI systems remains challenging, particularly for low-resource languages. While synthetic data offers promise, its effectiveness in multi…

cs.CL2025

MMTEB: Massive Multilingual Text Embedding Benchmark

Kenneth Enevoldsen, Isaac Chung, Imene Kerboua +83

Text embeddings are typically evaluated on a limited set of tasks, which are constrained by language, domain, and task diversity. To address these limitations and provide a more co…