Despite the increasing use of large language models (LLMs) for context-grounded tasks like summarization and question-answering, understanding what makes an LLM produce a certain response is challenging. We propose Multi-Level Explanations for Generative Language Models (MExGen), a technique to provide explanations for context-grounded text generation. MExGen assigns scores to parts of the context to quantify their influence on the model’s output. It extends attribution methods like LIME and SHAP to LLMs used in context-grounded tasks where (1) inference cost is high, (2) input text is long, and (3) the output is text. We conduct a systematic evaluation, both automated and human, of perturbation-based attribution methods for summarization and question answering. The results show that our framework can provide more faithful explanations of generated output than available alternatives, including LLM self-explanations. We open-source code for MExGen as part of the ICX360 toolkit: https://github.com/IBM/ICX360.

We present a synthetic data generation tool integrated into EvalAssist. EvalAssist is a web-based application designed to assist human-centered evaluation of language model outputs by allowing users to refine LLM-as-a-Judge evaluation criteria. The synthetic data generation tool in EvalAssist is tailored for evaluation contexts and informed by findings from user studies with AI practitioners. Participants identified key pain points in current workflows including circularity risks (where models are judged by criteria derived by themselves), compounded bias (amplification of biases across multiple stages of a pipeline), and poor support for edge cases, and expressed a strong preference for real-world grounding and fine-grained control. In response, our tool supports flexible prompting, RAG-based grounding, persona diversity, and iterative generation workflows. We also incorporate features for quality assurance and edge case discovery.