9 papers
Autoregressive Direct Preference Optimization
Masanari Oi, Mahiro Ukai, Masahiro Kaneko +2
Direct preference optimization (DPO) has emerged as a promising approach for aligning large language models (LLMs) with human preferences. However, the widespread reliance on the r…
Stopping Computation for Converged Tokens in Masked Diffusion-LM Decoding
Daisuke Oba, Danushka Bollegala, Masahiro Kaneko +1
Masked Diffusion Language Models generate sequences via iterative sampling that progressively unmasks tokens. However, they still recompute the attention and feed-forward blocks fo…
Multi-modal, Multi-task, Multi-criteria Automatic Evaluation with Vision Language Models
Masanari Ohi, Masahiro Kaneko, Naoaki Okazaki +1
Vision-language models (VLMs) have shown impressive abilities across a range of multi-modal tasks. However, existing metrics for evaluating the quality of text generated by VLMs ty…
Likelihood-based Mitigation of Evaluation Bias in Large Language Models
Masanari Oi, Masahiro Kaneko, Ryuto Koike +2
Large Language Models (LLMs) are widely used to evaluate natural language generation tasks as automated metrics. However, the likelihood, a measure of LLM's plausibility for a sent…
Intent-Aware Self-Correction for Mitigating Social Biases in Large Language Models
Panatchakorn Anantaprayoon, Masahiro Kaneko, Naoaki Okazaki
Self-Correction based on feedback improves the output quality of Large Language Models (LLMs). Moreover, as Self-Correction functions like the slow and conscious System-2 thinking…
How You Prompt Matters! Even Task-Oriented Constraints in Instructions Affect LLM-Generated Text Detection
Ryuto Koike, Masahiro Kaneko, Naoaki Okazaki
To combat the misuse of Large Language Models (LLMs), many recent studies have presented LLM-generated-text detectors with promising performance. When users instruct LLMs to genera…