works on

From the 1 of 55 linked papers with an AI index.

activity
20242026
collaborators

55 papers

cs.IR2026

With Argus Eyes: Assessing Retrieval Gaps via Uncertainty Scoring to Detect and Remedy Retrieval Blind Spots

Zeinab Sadat Taghavi, Ali Modarressi, Hinrich Schutze +1

The paper identifies blind spots in neural retrievers used for retrieval‑augmented generation, where relevant entities are missed due to low embedding similarity, and proposes an u…

cs.CL2026

Do We Still Need Humans in the Loop? Comparing Human and LLM Annotation in Active Learning for Hostility Detection

Ahmad Dawar Hakimi, Lea Hirlimann, Isabelle Augenstein +1

Instruction-tuned LLMs can annotate thousands of instances at low cost. This raises two questions for active learning (AL): can LLM labels replace human labels within the AL loop,…

cs.CL2026

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…

cs.CL2026

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…

cs.CL2026

Beyond Input Understanding: Diagnosing Multilingual Mathematical Reasoning with Directed Acyclic Trace Graphs

Jiaqiao Zhang, Zhoujun Li, Raoyuan Zhao +5

Large reasoning models (LRMs) achieve strong mathematical reasoning performance in English, but remain much less reliable in many low- and medium-resource languages. This gap is of…

cs.CL2026

ReverseMath: Answer Inversion for Scalable and Verifiable Mathematical Problem Generation

Raoyuan Zhao, Yihong Liu, Yupei Du +2

Mathematical reasoning benchmarks are vital for evaluating large language models (LLMs), but many are static and repeatedly exposed through public evaluation and training pipelines…