21 citations · 123 across the 41 of their papers we have counts for
41 papers · 1 filter
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…
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…
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…
Social Bias Evaluation for Large Language Models Requires Prompt Variations
Rem Hida, Masahiro Kaneko, Naoaki Okazaki
Warning: This paper contains examples of stereotypes and biases. Large Language Models (LLMs) exhibit considerable social biases, and various studies have tried to evaluate and mit…
Sampling-based Pseudo-Likelihood for Membership Inference Attacks
Masahiro Kaneko, Youmi Ma, Yuki Wata +1
Large Language Models (LLMs) are trained on large-scale web data, which makes it difficult to grasp the contribution of each text. This poses the risk of leaking inappropriate data…
Eagle: Ethical Dataset Given from Real Interactions
Masahiro Kaneko, Danushka Bollegala, Timothy Baldwin
Recent studies have demonstrated that large language models (LLMs) have ethical-related problems such as social biases, lack of moral reasoning, and generation of offensive content…
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…
Evaluating Gender Bias in Large Language Models via Chain-of-Thought Prompting
Masahiro Kaneko, Danushka Bollegala, Naoaki Okazaki +1
There exist both scalable tasks, like reading comprehension and fact-checking, where model performance improves with model size, and unscalable tasks, like arithmetic reasoning and…
The Gaps between Pre-train and Downstream Settings in Bias Evaluation and Debiasing
Masahiro Kaneko, Danushka Bollegala, Timothy Baldwin
The output tendencies of Pre-trained Language Models (PLM) vary markedly before and after Fine-Tuning (FT) due to the updates to the model parameters. These divergences in output t…
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…
SAIE Framework: Support Alone Isn't Enough -- Advancing LLM Training with Adversarial Remarks
Mengsay Loem, Masahiro Kaneko, Naoaki Okazaki
Large Language Models (LLMs) can justify or critique their predictions through discussions with other models or humans, thereby enriching their intrinsic understanding of instances…
Controlled Generation with Prompt Insertion for Natural Language Explanations in Grammatical Error Correction
Masahiro Kaneko, Naoaki Okazaki
In Grammatical Error Correction (GEC), it is crucial to ensure the user's comprehension of a reason for correction. Existing studies present tokens, examples, and hints as to the b…
The Impact of Debiasing on the Performance of Language Models in Downstream Tasks is Underestimated
Masahiro Kaneko, Danushka Bollegala, Naoaki Okazaki
Pre-trained language models trained on large-scale data have learned serious levels of social biases. Consequently, various methods have been proposed to debias pre-trained models.…
Evaluating Gender Bias of Pre-trained Language Models in Natural Language Inference by Considering All Labels
Panatchakorn Anantaprayoon, Masahiro Kaneko, Naoaki Okazaki
Discriminatory gender biases have been found in Pre-trained Language Models (PLMs) for multiple languages. In Natural Language Inference (NLI), existing bias evaluation methods hav…
In-Contextual Gender Bias Suppression for Large Language Models
Daisuke Oba, Masahiro Kaneko, Danushka Bollegala
Despite their impressive performance in a wide range of NLP tasks, Large Language Models (LLMs) have been reported to encode worrying-levels of gender biases. Prior work has propos…
OUTFOX: LLM-Generated Essay Detection Through In-Context Learning with Adversarially Generated Examples
Ryuto Koike, Masahiro Kaneko, Naoaki Okazaki
Large Language Models (LLMs) have achieved human-level fluency in text generation, making it difficult to distinguish between human-written and LLM-generated texts. This poses a gr…
Exploring Effectiveness of GPT-3 in Grammatical Error Correction: A Study on Performance and Controllability in Prompt-Based Methods
Mengsay Loem, Masahiro Kaneko, Sho Takase +1
Large-scale pre-trained language models such as GPT-3 have shown remarkable performance across various natural language processing tasks. However, applying prompt-based methods wit…
Reducing Sequence Length by Predicting Edit Operations with Large Language Models
Masahiro Kaneko, Naoaki Okazaki
Large Language Models (LLMs) have demonstrated remarkable performance in various tasks and gained significant attention. LLMs are also used for local sequence transduction tasks, i…
Solving NLP Problems through Human-System Collaboration: A Discussion-based Approach
Masahiro Kaneko, Graham Neubig, Naoaki Okazaki
Humans work together to solve common problems by having discussions, explaining, and agreeing or disagreeing with each other. Similarly, if a system can have discussions with human…
Comparing Intrinsic Gender Bias Evaluation Measures without using Human Annotated Examples
Masahiro Kaneko, Danushka Bollegala, Naoaki Okazaki
Numerous types of social biases have been identified in pre-trained language models (PLMs), and various intrinsic bias evaluation measures have been proposed for quantifying those…
Debiasing isn't enough! -- On the Effectiveness of Debiasing MLMs and their Social Biases in Downstream Tasks
Masahiro Kaneko, Danushka Bollegala, Naoaki Okazaki
We study the relationship between task-agnostic intrinsic and task-specific extrinsic social bias evaluation measures for Masked Language Models (MLMs), and find that there exists…
Are Neighbors Enough? Multi-Head Neural n-gram can be Alternative to Self-attention
Mengsay Loem, Sho Takase, Masahiro Kaneko +1
Impressive performance of Transformer has been attributed to self-attention, where dependencies between entire input in a sequence are considered at every position. In this work, w…
Gender Bias in Masked Language Models for Multiple Languages
Masahiro Kaneko, Aizhan Imankulova, Danushka Bollegala +1
Masked Language Models (MLMs) pre-trained by predicting masked tokens on large corpora have been used successfully in natural language processing tasks for a variety of languages.…
Gender Bias in Meta-Embeddings
Masahiro Kaneko, Danushka Bollegala, Naoaki Okazaki
Different methods have been proposed to develop meta-embeddings from a given set of source embeddings. However, the source embeddings can contain unfair gender-related biases, and…