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20132026
most citedOne Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques

270 citations · 1.1k across the 72 of their papers we have counts for

collaborators
Showing 2024 · cs.CLShow all

9 papers · 2 filters

cs.CL2024

Granite Guardian

Inkit Padhi, Manish Nagireddy, Giandomenico Cornacchia +20

We introduce the Granite Guardian models, a suite of safeguards designed to provide risk detection for prompts and responses, enabling safe and responsible use in combination with…

cs.CL2024

Graph-based Uncertainty Metrics for Long-form Language Model Outputs

Mingjian Jiang, Yangjun Ruan, Prasanna Sattigeri +2

Recent advancements in Large Language Models (LLMs) have significantly improved text generation capabilities, but these systems are still known to hallucinate, and granular uncerta…

cs.CL2024

Value Alignment from Unstructured Text

Inkit Padhi, Karthikeyan Natesan Ramamurthy, Prasanna Sattigeri +3

Aligning large language models (LLMs) to value systems has emerged as a significant area of research within the fields of AI and NLP. Currently, this alignment process relies on th…

cs.CL2024

When in Doubt, Cascade: Towards Building Efficient and Capable Guardrails

Manish Nagireddy, Inkit Padhi, Soumya Ghosh +1

Large language models (LLMs) have convincing performance in a variety of downstream tasks. However, these systems are prone to generating undesirable outputs such as harmful and bi…

cs.CL2024★ 1 cited

WikiContradict: A Benchmark for Evaluating LLMs on Real-World Knowledge Conflicts from Wikipedia

Yufang Hou, Alessandra Pascale, Javier Carnerero-Cano +5

Retrieval-augmented generation (RAG) has emerged as a promising solution to mitigate the limitations of large language models (LLMs), such as hallucinations and outdated informatio…

cs.CL2024

Large Language Model Confidence Estimation via Black-Box Access

Tejaswini Pedapati, Amit Dhurandhar, Soumya Ghosh +2

Estimating uncertainty or confidence in the responses of a model can be significant in evaluating trust not only in the responses, but also in the model as a whole. In this paper,…

cs.CL2024

Alignment Studio: Aligning Large Language Models to Particular Contextual Regulations

Swapnaja Achintalwar, Ioana Baldini, Djallel Bouneffouf +16

The alignment of large language models is usually done by model providers to add or control behaviors that are common or universally understood across use cases and contexts. In co…

cs.CL2024★ 2 cited

Language Models in Dialogue: Conversational Maxims for Human-AI Interactions

Erik Miehling, Manish Nagireddy, Prasanna Sattigeri +3

Modern language models, while sophisticated, exhibit some inherent shortcomings, particularly in conversational settings. We claim that many of the observed shortcomings can be att…

cs.CL2024

Multi-Level Explanations for Generative Language Models

Lucas Monteiro Paes, Dennis Wei, Hyo Jin Do +8

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 r…