32 papers
Macro: Enhancing Multilingual Counterfactual Explanations through Alignment-as-Preference Optimization
Yilong Wang, Qianli Wang, Bohao Chu +3
Self-generated counterfactual explanations (SCEs) are minimally modified inputs (minimality) generated by large language models (LLMs) that flip their own predictions (validity), o…
Reasoning over Grammar: Can Synthetic Linguistic Reasoning Traces Enhance Low-Resource Machine Translation?
Renhao Pei, Yihong Liu, Sampo Pyysalo +2
Large language models (LLMs) offer a promising approach to machine translation (MT) for extremely low-resource languages by incorporating linguistic resources through in-context le…
Expert-Aware Causal Tracing of Factual Recall in Sparse MoE Language Models
Yuetian Lu, Ali Modarressi, Yihong Liu +1
Causal tracing of factual recall has been studied predominantly in dense transformer language models, where interventions localize information flow to layers or feed-forward module…
Relational Linearity is a Predictor of Hallucinations
Yuetian Lu, Yihong Liu, Sebastian Gerstner +3
Hallucination is a central failure mode of language models (LMs). We focus on hallucinations in response to questions like: "Which instrument did Glenn Gould play?", but we ask the…
Understanding LLM Behavior in Multi-Target Cross-Lingual Summarization
Sangwon Ryu, Yihong Liu, Mingyang Wang +4
Multi-target cross-lingual text summarization (MTXLS), which summarizes a source document into multiple target languages, is increasingly important as users consume content in dive…
Calibration Is Not Enough: Evaluating Confidence Estimation Under Language Variations
Yuxi Xia, Dennis Ulmer, Terra Blevins +3
Confidence estimation (CE) indicates how reliable the answers of large language models are and impacts user trust and decision-making. Existing evaluations mainly concern the align…