4 citations · 6 across the 11 of their papers we have counts for
13 papers · 1 filter
Configurable Reward Model for Balanced Safety Alignment
Zhengping Jiang, Mehran Khodabandeh, Akash Bharadwaj +5
Aligning large language models (LLMs) to heterogeneous and rapidly evolving safety requirements remains a critical challenge. Existing instruction-tuned LLMs and standalone safety…
How Grounded is Wikipedia? A Study on Structured Evidential Support and Retrieval
William Walden, Kathryn Ricci, Miriam Wanner +4
Wikipedia is a critical resource for modern NLP, serving as a rich repository of up-to-date and citation-backed information on a wide variety of subjects. The reliability of Wikipe…
Always Tell Me The Odds: Fine-grained Conditional Probability Estimation
Liaoyaqi Wang, Zhengping Jiang, Anqi Liu +1
We present a state-of-the-art model for fine-grained probability estimation of propositions conditioned on context. Recent advances in large language models (LLMs) have significant…
CLAIMCHECK: How Grounded are LLM Critiques of Scientific Papers?
Jiefu Ou, William Gantt Walden, Kate Sanders +13
A core part of scientific peer review involves providing expert critiques that directly assess the scientific claims a paper makes. While it is now possible to automatically genera…
Conformal Linguistic Calibration: Trading-off between Factuality and Specificity
Zhengping Jiang, Anqi Liu, Benjamin Van Durme
Language model outputs are not always reliable, thus prompting research into how to adapt model responses based on uncertainty. Common approaches include: \emph{abstention}, where…
Core: Robust Factual Precision with Informative Sub-Claim Identification
Zhengping Jiang, Jingyu Zhang, Nathaniel Weir +6
Hallucinations pose a challenge to the application of large language models (LLMs) thereby motivating the development of metrics to evaluate factual precision. We observe that popu…
A Closer Look at Claim Decomposition
Miriam Wanner, Seth Ebner, Zhengping Jiang +2
As generated text becomes more commonplace, it is increasingly important to evaluate how well-supported such text is by external knowledge sources. Many approaches for evaluating t…
RORA: Robust Free-Text Rationale Evaluation
Zhengping Jiang, Yining Lu, Hanjie Chen +3
Free-text rationales play a pivotal role in explainable NLP, bridging the knowledge and reasoning gaps behind a model's decision-making. However, due to the diversity of potential…
Enhancing Systematic Decompositional Natural Language Inference Using Informal Logic
Nathaniel Weir, Kate Sanders, Orion Weller +8
Recent language models enable new opportunities for structured reasoning with text, such as the construction of intuitive, proof-like textual entailment trees without relying on br…
MegaWika: Millions of reports and their sources across 50 diverse languages
Samuel Barham, Orion Weller, Michelle Yuan +9
To foster the development of new models for collaborative AI-assisted report generation, we introduce MegaWika, consisting of 13 million Wikipedia articles in 50 diverse languages,…
Segmenting Subtitles for Correcting ASR Segmentation Errors
David Wan, Chris Kedzie, Faisal Ladhak +6
Typical ASR systems segment the input audio into utterances using purely acoustic information, which may not resemble the sentence-like units that are expected by conventional mach…
Subtitles to Segmentation: Improving Low-Resource Speech-to-Text Translation Pipelines
David Wan, Zhengping Jiang, Chris Kedzie +3
In this work, we focus on improving ASR output segmentation in the context of low-resource language speech-to-text translation. ASR output segmentation is crucial, as ASR systems s…
Uncertain Natural Language Inference
Tongfei Chen, Zhengping Jiang, Adam Poliak +2
We introduce Uncertain Natural Language Inference (UNLI), a refinement of Natural Language Inference (NLI) that shifts away from categorical labels, targeting instead the direct pr…