most citedWhen LLMs Struggle: Reference-less Translation Evaluation for Low-resource Languages

1 citations · 1 across the 7 of their papers we have counts for

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7 papers

q-bio.NC2025

Human-Centred Evaluation of Text-to-Image Generation Models for Self-expression of Mental Distress: A Dataset Based on GPT-4o

Sui He, Shenbin Qian

Effective communication is central to achieving positive healthcare outcomes in mental health contexts, yet international students often face linguistic and cultural barriers that…

cs.CL2025

The Mind's Eye: A Multi-Faceted Reward Framework for Guiding Visual Metaphor Generation

Girish A. Koushik, Fatemeh Nazarieh, Katherine Birch +2

Visual metaphor generation is a challenging task that aims to generate an image given an input text metaphor. Inherently, it needs language understanding to bind a source concept w…

cs.CL2025

ALOPE: Adaptive Layer Optimization for Translation Quality Estimation using Large Language Models

Archchana Sindhujan, Shenbin Qian, Chan Chi Chun Matthew +2

Large Language Models (LLMs) have shown remarkable performance across a wide range of natural language processing tasks. Quality Estimation (QE) for Machine Translation (MT), which…

cs.IR2025

NEAR: A Nested Embedding Approach to Efficient Product Retrieval and Ranking

Shenbin Qian, Diptesh Kanojia, Samarth Agrawal +4

E-commerce information retrieval (IR) systems struggle to simultaneously achieve high accuracy in interpreting complex user queries and maintain efficient processing of vast produc…

cs.CL2025

Automatically Generating Chinese Homophone Words to Probe Machine Translation Estimation Systems

Shenbin Qian, Constantin Orăsan, Diptesh Kanojia +1

Evaluating machine translation (MT) of user-generated content (UGC) involves unique challenges such as checking whether the nuance of emotions from the source are preserved in the…

cs.CL20251 cited

When LLMs Struggle: Reference-less Translation Evaluation for Low-resource Languages

Archchana Sindhujan, Diptesh Kanojia, Constantin Orasan +1

This paper investigates the reference-less evaluation of machine translation for low-resource language pairs, known as quality estimation (QE). Segment-level QE is a challenging cr…