7 citations · 8 across the 14 of their papers we have counts for
14 papers
HyperTrace: Hypothesis-Based Preference Tracing for Online LLM Personalization
Jianzhi Shen, Keyu Mao, Minghao Shao +7
Personalized language models aim to adapt responses to individual users, whose preferences are often latent and revealed gradually through interaction. Existing training-free metho…
When Do LLMs Apply the Wrong Law? Diagnosing LLM Failures in Temporal Legal Reasoning
Yiqian Huang, Shuyuan Zheng, Yang Zhou +7
Legal reasoning requires identifying which version of a statute applies to a case, typically based on when the legally relevant events occurred rather than when the case is adjudic…
ReverseEOL: Improving Training-free Text Embeddings via Text Reversal in Decoder-only LLMs
Ailiang Lin, Zhuoyun Li, Yusong Wang +3
Recent advances in Large Language Models (LLMs) have opened new avenues for generating training-free text embeddings. However, the causal attention in decoder-only LLMs prevents ea…
Can LLMs Be Constrained to the Past? Improving Knowledge Cutoff through Recall-Based Prompting
Michiro Asai, Ailiang Lin, Yu Kishimoto +4
Prompted knowledge cutoff instructs a large language model (LLM) to act as if information beyond a specified cutoff date were unavailable. However, prior work mainly relies on dire…
Embedding-based In-Context Prompt Training for Enhancing LLMs as Text Encoders
Ailiang Lin, Zhuoyun Li, Keyu Mao +2
Large language models (LLMs) have been widely explored for embedding generation. While recent studies show that in-context learning (ICL) effectively enhances the representational…
Minimum Bayes Risk Decoding for Error Span Detection in Reference-Free Automatic Machine Translation Evaluation
Boxuan Lyu, Haiyue Song, Hidetaka Kamigaito +5
Error Span Detection (ESD) extends automatic machine translation (MT) evaluation by localizing translation errors and labeling their severity. Current generative ESD methods typica…
Emotion Recognition in Multi-Speaker Conversations through Speaker Identification, Knowledge Distillation, and Hierarchical Fusion
Xiao Li, Kotaro Funakoshi, Manabu Okumura
Emotion recognition in multi-speaker conversations faces significant challenges due to speaker ambiguity and severe class imbalance. We propose a novel framework that addresses the…
Causal2Vec: Improving Decoder-only LLMs as Embedding Models through a Contextual Token
Ailiang Lin, Zhuoyun Li, Yusong Wang +2
Decoder-only large language models (LLMs) have been increasingly adopted to build embedding models for diverse tasks. To overcome the inherent limitations of causal attention in re…
Contrastive Knowledge Distillation for Robust Multimodal Sentiment Analysis
Zhongyi Sang, Kotaro Funakoshi, Manabu Okumura
Multimodal sentiment analysis (MSA) systems leverage information from different modalities to predict human sentiment intensities. Incomplete modality is an important issue that ma…
Unveiling the Power of Source: Source-based Minimum Bayes Risk Decoding for Neural Machine Translation
Boxuan Lyu, Hidetaka Kamigaito, Kotaro Funakoshi +1
Maximum a posteriori decoding, a commonly used method for neural machine translation (NMT), aims to maximize the estimated posterior probability. However, high estimated probabilit…
DiLM: Distilling Dataset into Language Model for Text-level Dataset Distillation
Aru Maekawa, Satoshi Kosugi, Kotaro Funakoshi +1
Dataset distillation aims to compress a training dataset by creating a small number of informative synthetic samples such that neural networks trained on them perform as well as th…
Joyful: Joint Modality Fusion and Graph Contrastive Learning for Multimodal Emotion Recognition
Dongyuan Li, Yusong Wang, Kotaro Funakoshi +1
Multimodal emotion recognition aims to recognize emotions for each utterance of multiple modalities, which has received increasing attention for its application in human-machine in…
Active Learning Based Fine-Tuning Framework for Speech Emotion Recognition
Dongyuan Li, Yusong Wang, Kotaro Funakoshi +1
Speech emotion recognition (SER) has drawn increasing attention for its applications in human-machine interaction. However, existing SER methods ignore the information gap between…
Automatic Answerability Evaluation for Question Generation
Zifan Wang, Kotaro Funakoshi, Manabu Okumura
Conventional automatic evaluation metrics, such as BLEU and ROUGE, developed for natural language generation (NLG) tasks, are based on measuring the n-gram overlap between the gene…