SLoW selects low-frequency word dictionaries to boost LLM translation quality and efficiency across 100 languages from FLORES.
ISBN 9781450380959
7 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
representative citing papers
MLLMs given the same instructions as human participants achieve expert-level performance on perceiving stress in network visualizations and rely on similar visual proxies.
DIP interleaves English word translations into non-English prompts to boost multilingual reasoning on synthetic benchmarks spanning 10-200 languages.
iPOE derives and optimizes guidelines from explanations to create interpretable prompts, yielding up to 31% and 35% gains over standard and random-guideline prompts on four datasets.
LLMs share task-specific attention heads across prompting styles, with activation strength explaining performance differences and failures arising from competing representations.
Neural Computers are introduced as a new machine form where computation, memory, and I/O are unified in a learned runtime state, with initial video-model experiments showing acquisition of basic interface primitives from traces.
The study compares MLLM-generated usability evaluations against expert assessments on prioritization of issues and introduces an interactive visualization tool for reviewing model outputs.
citing papers explorer
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SLoW: Select Low-frequency Words! Automatic Dictionary Selection for Translation on Large Language Models
SLoW selects low-frequency word dictionaries to boost LLM translation quality and efficiency across 100 languages from FLORES.
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Exploring MLLMs Perception of Network Visualization Principles
MLLMs given the same instructions as human participants achieve expert-level performance on perceiving stress in network visualizations and rely on similar visual proxies.
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Dictionary Insertion Prompting for Multilingual Reasoning on Multilingual Large Language Models
DIP interleaves English word translations into non-English prompts to boost multilingual reasoning on synthetic benchmarks spanning 10-200 languages.
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iPOE: Interpretable Prompt Optimization via Explanations
iPOE derives and optimizes guidelines from explanations to create interpretable prompts, yielding up to 31% and 35% gains over standard and random-guideline prompts on four datasets.
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Shared Lexical Task Representations Explain Behavioral Variability In LLMs
LLMs share task-specific attention heads across prompting styles, with activation strength explaining performance differences and failures arising from competing representations.
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Neural Computers
Neural Computers are introduced as a new machine form where computation, memory, and I/O are unified in a learned runtime state, with initial video-model experiments showing acquisition of basic interface primitives from traces.
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Investigating Multimodal Large Language Models to Support Usability Evaluation
The study compares MLLM-generated usability evaluations against expert assessments on prioritization of issues and introduces an interactive visualization tool for reviewing model outputs.